CHARACTERIZATION, POLYMORPHISM ASSESSMENT, AND DATABASE CONSTRUCTION FOR MICROSATELLITES FROM BAC END SEQUENCES OF CATFISH: A RESOURCE FOR INTEGRATION OF LINKAGE AND PHYSICAL MAPS Except where reference is made to the work of others, the work described in this thesis is my own or was done in collaboration with my advisory committee. This thesis does not include proprietary or classified information. ________________________________ Benjaporn Somridhivej Certificate of Approval: ___________________________ ___________________________ Yolanda J. Brady Zhanjiang (John) Liu, Chair Associate Professor Alumni Professor Fisheries and Allied Aquacultures Fisheries and Allied Aquacultures ___________________________ ___________________________ Nannan Liu George T. Flowers Associate Professor Interim Dean Entomology and Plant Pathology Graduate School CHARACTERIZATION, POLYMORPHISM ASSESSMENT, AND DATABASE CONSTRUCTION FOR MICROSATELLITES FROM BAC END SEQUENCES OF CATFISH: A RESOURCE FOR INTEGRATION OF LINKAGE AND PHYSICAL MAPS Benjaporn Somridhivej A Thesis Submitted to the Graduate Faculty of Auburn University in Partial Fulfillment of the Requirements for the Degree of Master of Science Auburn, Alabama May 10, 2007 iii CHARACTERIZATION, POLYMORPHISM ASSESSMENT, AND DATABASE CONSTRUCTION FOR MICROSATELLITES FROM BAC END SEQUENCES OF CATFISH: A RESOURCE FOR INTEGRATION OF LINKAGE AND PHYSICAL MAPS Benjaporn Somridhivej Permission is granted to Auburn University to make copies of this thesis at its discretion, upon request of individuals or institutions and at their expense. The author reserves all publication rights. _________________________ Signature of Author _________________________ Date of Graduation iv VITA Benjaporn Somridhivej, daughter of Kosol Somridhivej and Smurchai Somridhivej, was born on August 26, 1974 in Bangkok, Thailand. She graduated from Bodindecha (Sing Singhaseni) high school, Bangkok, Thailand in 1993. She entered Kasetsart University, Bangkok, Thailand where she graduated with a Bachelor of Science in Fisheries Biology in 1997. After working as a fisheries biologist at the Department of Fisheries, Bangkok, Thailand for five years, she enrolled in Graduate School at Auburn University to pursue a Master of Science degree in Department of Fisheries and Allied Aquacultures in May 2005. v THESIS ABSTRACT CHARACTERIZATION, POLYMORPHISM ASSESSMENT, AND DATABASE CONSTRUCTION FOR MICROSATELLITES FROM BAC END SEQUENCES OF CATFISH: A RESOURCE FOR INTEGRATION OF LINKAGE AND PHYSICAL MAPS Benjaporn Somridhivej Master of Science, May 10, 2007 (B.Sc., Kasetsart University, 1997) 71 Typed Pages Directed by Zhanjiang (John) Liu To apply genome-based technologies for genetic improvements using marker- assisted selection, genome research involving genetic linkage mapping and physical mapping is required, and integration of genetic and linkage maps would significantly enhance the capacities for genome research. In catfish, the major aquaculture species in the United States, linkage and physical maps have been constructed. However, integration of genetic linkage and physical maps demands large-scale, genome-wide hybridizations, or genetic mapping of polymorphic markers derived from bacterial artificial chromosome (BAC) clones whose location is known from the physical map. vi In this work, we identified a large number of microsatellites from BAC end sequences of channel catfish, characterized the microsatellites, tested their utility for linkage mapping in a resource family used for genetic mapping, and constructed a web- searchable database for BAC end sequences, their linked microsatellites, microsatellite primers, PCR conditions, and polymorphic information. A total of 2,744 distinct BACs containing microsatellites were identified. Of these, 1,100 had sufficient and complex flanking sequences for PCR primer design. We have tested 500 primer pairs and found 211 (42.2%) were polymorphic and segregating in the resource family used for genetic mapping. These microsatellites represent a major fraction of co-dominant polymorphic markers identified to date in catfish, and should be a valuable resource for genetic mapping to increase linkage map resolution, and for integration of genetic linkage and physical maps. vii ACKNOWLEDGEMENTS The author would like to thank her major professor, Dr. Zhanjiang (John) Liu, for his instruction, support, enthusiasm and guidance throughout the learning processes. The author would also like to extend her thanks to lab members, Dr. Huseyin Kucuktas, Ping Li, Dr. Puttharat Baoprasertkul, Dr. Eric Peatman, Xu Peng, Wang Saolin and Jason Abernathy for their assistance and invaluable friendship. Moreover, the author is especially indebted to Dr. Yolanda Brady and Dr. Nannan Liu for taking the time to be on her thesis committee. Special thanks go to all her friends that gave her support and help. Finally, the author would like to thank her parents and all members of her family for their understanding, encouragement, and support. viii Style manual used Aquaculture Computer software used Microsoft Word 2002, Microsoft Excel 2002, Adobe Photoshop 6.0, FASTPCR and Msatfinder ix TABLE OF CONTENTS LIST OF TABLES????????.????????????????...............x LIST OF FIGURES??????????????????????..?...............xi I. INTRODUCTION????????????????????????.............1 II. CHARACTERIZATION, POLYMORPHISM ASSESSMENT, AND DATABASE CONSTRUCTION FOR MICROSATELLITES FROM BAC END SEQUENCES OF CATFISH: A RESOURCE FOR INTEGRATION OF LINKAGE AND PHYSICAL MAPS....................................................................................................................................9 Introduction???????????????????????.............9 Materials and Methods ?????????????????????.12 Results?????????????????????????...........18 Discussion??????????????????????????.26 III. CONCLUSIONS???????????????..???????????.31 REFERENCES?????????????????????????????35 x LIST OF TABLES Table 1. A summary of the microsatellites identified from BAC end sequences?.........46 Table 2. Assessing the utility of the BAC-anchored microsatellites for linkage mapping???????????????????????????????..47 Table 3. The number and polymorphism tested from various types of microsatellites....49 Table 4. Microsatellites identified from BAC end sequences. P indicates polymorphism, NP indicates no polymorphism, empty cells indicate no PCR products were generated with fidelity such that genotyping was not possible??????????????..50 xi LIST OF FIGURES FIGURE 1. Percentage of di-, tri-, and tetra-nucleotide repeats identified from BAC end sequences of catfish.?..??????????????????????.????.20 FIGURE 2. Distribution of various types of dinucleotide repeats identified from BAC end sequences of catfish. Note the low representation of G/C-rich types?????.................21 FIGURE 3. Distribution of various types of trileotide repeats identified from BAC end sequences of catfish. Note that A/T-rich types are highly abundant. ????...????22 FIGURE 4. Distribution of various types of tetranucleotide repeats identified from BAC end sequences of catfish. Note that A/T-rich types are highly abundant.?????????.24 FIGURE 5. Comparison of polymorphic rates of di-, tri-, and tetra-nucleotide repeats within the resource family.???????????????...?????????.??.....26 FIGURE 6. Comparison of polymorphic rates of various dinucleotide repeats within the resource family.??????????????..???????????.????27 1 I. INTRODUCTION Catfish is the most important cultured fish in the U.S. and accounts for over 50% of all U.S. aquaculture production. The catfish industry is valued at 2 to 3 billion dollars and production last year exceeded 700 million pounds. In the State of Mississippi and Alabama, catfish is one of the top agricultural commodities and it is extremely important for development of rural America in the Southeast and other areas in the U.S. Despite the development of the aquaculture and catfish industry in the U.S., a large trade deficit, 8 billion dollars annually, exists for seafood products. It is disappointing that aquaculture products are the third largest trade deficit contributor following petroleum and automobiles. It is time to address our aquaculture problems, especially considering the collapsing natural fisheries. According to USDA estimates, the U.S. demand for seafood is increasing steadily and wild fisheries will be able to supply only 25% to 30% of the additional demand. Trade deficit for seafood products is expected to increase. One way of increasing seafood supply is to increase marine fishing, but the world fish stocks are in crisis. Almost two-thirds of marine stocks in the Pacific and Atlantic Oceans are being fully exploited or have already been overfished. Future projections predict a steadily widening gap between the world?s demand for fish and the ability of the oceans to meet it . The solution lies in development of aquaculture. 2 Several problems severely limit development of the catfish industry. Diseases cause the largest amount of loss in catfish industry. Superior brood stocks resistant to major diseases are desperately needed. Although a rich resource exists among Ictalurid catfish for resistance to major diseases such as Enteric septicemia of catfish (ESC) and columnaris, for fast growth and for high carcass yield, genomic research is required to introgress these genes for combined benefits. Resistance- and carcass yield-linked markers are especially needed for marker-assisted selection. The major problems of the catfish industry are related to the low profit margins. As a matter of fact, catfish producers lost money in the last a couple of years because of very low catfish prices. Among many things, improving performance and production traits could potentially reduce production cost and, therefore, increase profit margins. Of many performance and production traits, the most important ones include growth rate, feed conversion efficiency, disease resistance, tolerance to low dissolved oxygen, tolerance to low water quality, processing yields, and seinability. Much progress has been made in improving these traits through various means. Disease resistance was improved through interspecific hybridization (Dunham et al., 1990; Dunham, 1996), intraspecific crossbreeding and strain selection (Wolters and Johnson, 1994). Efforts to improve growth rate were made through selection (Bondari, 1983; Dunham and Smitherman, 1983a), intraspecific crossbreeding (Dunham and Smitherman, 1983b; Bondari 1983), and interspecific hybridization (Dunham et al., 1990; Dunham, 1996); tolerance to low dissolved oxygen was improved through interspecific hybridization (Dunham et al., 1983b; Dunham, 1996); seinability was improved by interspecific hybridization (Dunham, 1996) and strain selection. It is believed that traditional selection 3 methods will continue to make major contributions to improving the genetic quality of broodstocks. However, several limitations of the traditional selection demands development of new selection approaches. For instance, a selected broodstock may not harbor the desired gene; accurate measurement of quantitative traits is difficult and progeny testing of the selected traits may require great efforts; selection for some important traits such as disease resistance and carcass yield may be impractical. Challenging fish with disease in a production environment is not desirable. Direct selection for carcass yield is lethal to the broodstocks. All these limitations demands novel approaches such as marker-assisted selection (MAS) in aquaculture species. Marker-assisted selection is a selection procedure based on the presence or absence of specific DNA markers that have been previously identified to be linked to the performance traits under consideration. While traditional selective breeding is based on phenotypic observations, MAS is based on DNA markers. For instance, if a specific DNA marker is already known to be linked to disease resistance, then brood fish can be selected based on if the fish harbors this specific marker without any disease challenges. MAS offers a more accurate selection of the desired genotype by using the linkage information of a molecular marker and a certain phenotype. Marker-assisted selection requires linkage information of molecular markers and the performance or production traits. Such information can be obtained through linkage mapping of quantitative trait loci (QTL) because most, if not all, of these traits are controlled by many genes and, therefore, are inherited in a quantitative fashion. In doing so, a large number of molecular markers are needed to construct a genetic linkage map. Certain genomic information is helpful for the development of molecular markers 4 including genomic sizes (both physical and recombination sizes), polymorphic rates, and availability and applicability of various molecular makers in the species of interest. Channel catfish has 29 pairs of chromosomes (LeGrande et al., 1984) and a genome size of 1.0 x 10 9 base pairs (Tiersh et al., 1990; Tiersh and Goudie, 1993). Advances in molecular biology and instrumentation facilitated the rapid development of molecular markers. A molecular marker is a site of heterozygosity for some type of neutral DNA variation because it easily detected and numerous in a genome. When mapped by linkage analysis it fill voids between genes of known genome. DNA marker is a specific, unique sequence of DNA that can be detected, identified and tracked a location on the chromosome. At the beginning, genes were used as marker on genetic mapping but the problem is map based on genes is not very detailed. To dated, several types of DNA markers have been used in mapping including allozyme markers, restriction fragment length polymorphism (RFLP), randomly amplified polymorphic DNA (RAPD), amplified fragment length polymorphism, microsatellites, and single nucleotide polymorphism (SNP). Allozyme markers are type I markers and should be highly useful as anchorage points for comparative mapping. Variation is detected at the protein level, and the markers are co-dominant. However, total number of polymorphic loci is small and polymorphic rates are low at each locus. Restriction fragment length polymorphism markers (RFLPs) are co-dominant markers. They are easy to score. However, they have low levels of polymorphism; they are time-consuming and laborious; probes and/or sequence information are required. Because of these limitations, RFLP markers are rare in catfish. 5 Randomly amplified polymorphic (RAPD) is a method of creating genomic fingerprints using short arbitrary primers and PCR. It is suitable for, but not limited to, species of which little molecular genetic information is known (Welsh and McClelland, 1990; Williams et al., 1990). It is technically easy and highly economical; and polymorphism levels are very high. However RAPD has low reproducibility because of low annealing temperature used during PCR. RAPD markers are inherited as dominant markers. In catfish, Liu et al., (1999a) found that polymorphic rates of RAPD are low among strains of channel catfish, but high between channel catfish and blue catfish. More than 600 RAPD markers were identified in catfish (Liu et al., 1998a). Amplified fragment length polymorphism (AFLP) is a PCR-based fingerprinting technique (Vos et al., 1995). In contrast to RAPD, AFLP uses long primers during PCR and, therefore, is much more reproducible. AFLP is robust, reliable, powerful, economical, and applicable to all species as previous genetic information is not required. However, AFLPs are inherited as dominant markers. Microsatellites are simple sequence repeats (SSR) of 1-6 bp long. Microsatellites have even distributions on all chromosomes though abundance varies with species. Microsatellites are highly polymorphic and co-dominant markers making it highly reliable and useful for linkage mapping. Microsatellite loci are generally short so genotyping can be facilitated by PCR. Liu et al. (1999b) found that most of the microsatellite-flanking sequences have been conserved across the genus borders of the Ictalurid catfish. This will allow the development of comprehensive linkage map using interspecific hybrid system and various types of markers (Liu and Feng, 2001). 6 Microsatellites are abundant and distributed on all chromosomes. These microsatellites have high level of polymorphism (Litt and Luty1989; Weber and May 1989; Tautz 1989) and are co-dominant markers; therefore, they are highly reliable. The Microsatellite analysis requires a primer pair for each marker locus, but these primer sequences can easily be shared throughout the world and rapidly be constructed by using a DNA synthesizer. The microsatellite loci is generally short, which makes it easy to generated by PCR and the result obtained can be observed by electrophoretic method. The variation of microsatellite loci is considered by differences in the number of repeating units in DNA segments. Microsatellites have proven to be very useful for many purposes; namely, estimating genetic variation in natural populations (Bruford and Wayne 1993), studying paternity, identifying any individual, genetic and linkage mapping. Genome research also requires understanding of the physical organization of the genome. Most often, such understanding is achieved through physical mapping using bacterial artificial chromosome (BAC) libraries. BAC contain large genomic DNA insert of 100-250 kb, and therefore, a single genome equivalent can be included in approximately 5000-6000 BAC clones. A typical BAC library contain 6 to 15X genome coverage of genomic DNA. In catfish, a BAC library, CHORI 212, was constructed and characterized (Wang et al., 2005). Fingerprints of BAC clones would allow them to be arrayed into contigs: a series of BAC clones overlapping one another spanning a large segments of the chromosome. The catfish physical map contains approximately 3000 contigs (Xu et al., in review). 7 BAC end sequences of microsatellites are more suitable than other resources because they not only provide an unbiased survey of genomic sequence, but also allow an overall glance at the types and relative abundance of microsatellites in an organism. BAC end sequences can be used for identifying conserved synteny for comparative genome analysis to observe evolution, construct physical map and integrate genetic linkage map. The genetic linkage mapping is a map that show genetic distance of gene related to each other in each chromosome. This distance is called centimorgan (cM). The genetic linkage map can be analyzed by using recombinant frequency from linkage analysis. A recombinant frequency (RF) of 1% is equivalent to 1 cM. To construct genome maps, genetic linkage mapping techniques such as marker segregation followed by analysis of recombination frequency can be used. Physical mapping is a map of the position of a cloned genomic fragment that identifies landmark on DNA by using molecular biology techniques. The purpose is to identify a set of overlapping cloned fragments that together encompass an entire chromosome or an entire genome (Griffiths et al., 1999). Integration of genetic and linkage maps can be approached in two different ways. First, DNA markers that have already been mapped to genetic linkage maps can be used as probes to hybridize to high-density BAC filters. This approach can be made more effective by the adoption of two dimensional hybridizations (Han et al., 2000; Gardiner et al., 2004), but can be complicated by the presence of repetitive sequences, gene families, and pseudo-genes associated with the probes. While efforts have been devoted to hybridization studies in catfish (Bao et al., 2005; Peatman et al., 2006), several major 8 technical problems limit large-scale, genome-wide hybridization of microsatellite markers to BAC contigs. Second, polymorphic DNA markers can be developed from the known locations on the physical maps, but there is no polymorphic markers available before my work for this purpose. The objective of this work is to generate polymorphic markers derived from BAC clones that have been physically mapped so that these polymorphic markers can be mapped genetically on the linkage map. Specifically, the objectives of this study are: a) To identify microsatellites from BAC end sequences through data mining; b) To characterize microsatellites identified from the BAC end sequences concerning repeat types, microsatellite repeat numbers, location within the BAC end sequences, flanking sequences, and a distinct set of BACs containing microsatellites; c) To test polymorphism of BAC-derived microsatellites in our resource family used for the construction of the genetic linkage map by using PCR analysis and determination of their segregation among individuals of the resource family; and d) To develop a database for the BAC-anchored microsatellites, making them a useful resource for the integration of the genetic linkage and physical map 9 II. CHARACTERIZATION, POLYMORPHISM ASSESSMENT, AND DATABASE CONSTRUCTION FOR MICROSATELLITES FROM BAC END SEQUENCES OF CATFISH: A RESOURCE FOR INTEGRATION OF LINKAGE AND PHYSICAL MAPS 1. Introduction The major objectives of structural genomics are to elucidate genome structure, organization, and evolution (O?Brien et al., 1991). These issues are approached by linkage and physical mapping, genome sequencing, and comparative genome analysis. Linkage and physical mapping, in particular, provide a framework for the understanding of genome organization and set the foundation for whole genome sequencing. Thus, linkage maps have been constructed from various aquaculture species including rainbow trout (Young et al., 1998; Sakamoto et al., 2000; Nichols et al., 2003; Danzmann et al., 2005), Atlantic salmon (Moen et al., 2004; Gilbey et al., 2004), tilapia (Kocher et al., 1998; Agresti et al., 2000; Lee et al., 2005), channel catfish (Waldbieser et al., 2001; Liu et al., 2003), European sea bass (Chistiakov et al., 2005), sea bream (Franch et al., 2006), common carp (Sun and Liang, 2004), shrimps (Moore et al., 1999; Wilson et al., 2002; Li et al. 2003; P?rez et al., 2004; Li et al., 2006), oysters (Yu and Guo, 2003; Hubert and Hedgecock, 2004; Li and Guo, 2004), scallops (Li et al., 2005; 10 Wang et al., 2005), and abalone (Sekino et al., 2006; 2007; Liu et al., 2006). Similarly, efforts toward the construction of physical maps have been made in aquaculture species including the construction of large insert BAC libraries in Atlantic salmon (Thorsen et al., 2005), tilapia (Katagiri et al., 2001), and catfish (Quiniou et al., 2003; Wang et al., in review), and the construction of BAC-based physical maps in Atlantic salmon (Ng et al., 2005), tilapia (Katagiri et al., 2005), and channel catfish (Xu et al., in review). In genetic linkage mapping, genome organization is characterized by the analysis of marker relationships. Markers on the same chromosome tend to segregate together as they are physically linked to one another. However, recombination frequency increases as the distances among markers increase. The recombination frequency, therefore, has been used to order markers on a chromosome. Use of a large number of markers, therefore, allows construction of detailed genetic linkage maps that can place genetic markers on the genome. Genetic maps, however, are purely based on genetic distances in relation to genetic recombination frequency. In spite of the generally parallel relationship between genetic distance and physical distance, recombination frequency can vary greatly among organisms, or among various genome regions within an organism. In addition, once a trait is mapped genetically, the only information known is the distance of this trait to certain markers. Without a physical map, further studies and analysis of the gene controlling the trait is hindered. In contrast to the situation of genetic linkage mapping, physical maps are constructed using physical pieces of DNA. In most cases, whole genome physical maps are constructed using large-insert BAC contigs, where overlapping BAC clones are ordered by their overlapping patterns of restriction enzyme fingerprints. With a well- 11 developed physical map, accurate distances between any of the BAC clones can be obtained. However, physical maps lack genetic information concerning performance traits that can only be mapped genetically. Therefore, the integration of genetic and physical maps becomes essential for the identification and analysis of the genes underlining performance traits. Once the traits or traits-linked markers are mapped to physical maps, the exact DNA sequences between a set of markers mapped in the proximity of performance traits can be decoded by DNA sequencing. For aquaculture research, linkage mapping allows connection of performance or production traits with genomic regions, while physical mapping establishes the relationships of physical segments of DNA for further characterization. Integration of genetic linkage map with physical map would allow performance traits to be placed on physical intervals of DNA segments, whereby candidate genes can be identified and characterized. Integration of genetic and linkage maps can be approached in two different ways. First, DNA markers that have already been mapped to genetic linkage maps can be used as probes to hybridize to high-density BAC filters. This approach can be made more effective by the adoption of two dimensional hybridizations (Han et al., 2000; Gardiner et al., 2004), but can be complicated by the presence of repetitive sequences, gene families, and pseudo-genes associated with the probes. While efforts have been devoted to hybridization studies in catfish (Bao et al., 2005; Peatman et al., 2006), several major technical problems limit large-scale, genome-wide hybridization of microsatellite markers to BAC contigs. Second, polymorphic DNA markers can be developed from the known locations on the physical maps. In this approach, polymorphic markers such as microsatellites can be identified from BAC clones that are already fingerprinted for the 12 construction of physical maps. The polymorphic markers can then be genetically mapped by analysis using the resource families that were constructed for linkage mapping. In channel catfish, various markers have been developed including microsatellite markers (Liu et al., 1998; Tan et al., 1999, for review, see Liu, 2003). In particular, the identification of a large number of microsatellites from expressed sequence tags (ESTs) (Serapion et al., 2004) has allowed the construction of a gene-based genetic linkage map useful for comparative genome analysis (Liu et al., in review). Linkage maps have been constructed using microsatellite markers (Waldbieser et al., 2001; Liu et al., in review). Recently, a BAC contig-based physical map has been constructed using the CHORI 212 BAC library (Xu et al., in review). As discussed above, integration of the genetic linkage map and the physical map would significantly enhance the capacities in catfish genome research. However, BAC-anchored markers have been lacking for map integration. In order to develop BAC-anchored microsatellite markers, we have initiated a BAC end sequencing project (Xu et al., 2006). Over 20,000 BAC end sequences have been produced (Xu et al., 2006). In this project, our objectives were to characterize microsatellites identified from the BAC end sequences, to test their polymorphism in our resource family used for the construction of the genetic linkage map, and to develop a database for the BAC-anchored microsatellites, making them a useful resource for the integration of the genetic linkage and physical maps. 13 2. Materials and Methods 2.1. Mining microsatellites from BAC end sequences The FASTA file of the BAC end sequences was downloaded from NCBI and stored on the local computer for microsatellite mining. A Perl-based script Msatfinder (freeware, downloaded from http://www.genomics.ceh.ac.uk/msatfinder/) was used for microsatellite mining from the FASTA file (Thurston and Field, 2005). Msatfinder was run on the Linux operation system (Fedora Core 5). Msatfinder examines sequence files in GenBank, FASTA, EMBL and Swissprot formats, and determines the number, type and position of microsatellite repeats. The parameters were set at default for microsatellite searching: minimum-repeat number was set at eight for mono-and di- nucleotide repeats, and at five for tri- to hexa-nucleotide repeats. As mononucleotide repeats are not useful for mapping, they were manually excluded from the search output file. Searches were conducted following Msatfinder Manual (http://www.genomics.ceh.ac.uk/msatfinder/msatfinder_manual.html) as the following: The search results were stored in the directories, Counts, Fasta, Flank_tabs, MINES, Msat_tabs Primers and Repeats. The first five directories included the motif, type, sequence, primer information and database files for each individual microsatellite. All the summary files were stored in the Repeats directory containing seven files. The index file is a handy summary of the results with the total motif types and numbers found in the total sequences. The sequence file contains the information on each sequence, including number of microsatellites found, GC content and so on. The repeats file contains the 14 details of every individual microsatellite, including the type and number of motif, the location of the microsatellites, plus similar genomic information to the sequence file. Both this file and the sequence file may easily be imported into Excel, or imported into a database. The type.count file showed the number of microsatellites found, categorized by motif type (mono-, di-, tri-, tetra-, penta-, and hexa-nucleotide repeats), and the number of repeat units. The motif.count file is similar to the type.count file, which shows the number of microsatellites found categorized by the bases/amino acids in the motif. These are ordered by the total base content only, thus AAT would be counted the same as TTA (exact summaries are available in the Counts/directory). The primers.csv file is a tabular summary of the information in all the primer files in the Primers/ directory. The errors file contains details of anything that looked unusual, e.g. very short sequences, features that did not match the sequence, and the summary of motif type and numbers. 2.2. Characterization of microsatellites The index file was used to summarize the total motif types and numbers from the BAC end sequences and the distribution of motif types within the di-, tri- and tetra- nucleotide repeats. The repeat file with the microsatellite information was imported into Excel to get all the microsatellite information. All mononucleotide microsatellites were removed before analysis. First, the microsatellite-containing BAC end sequences were searched to determine whether they had sufficient flanking sequences for primer design by harboring at least 50 bp flanking sequences on either side of microsatellites. The file was sorted by start points, microsatellites were excluded if the start position began from 1-50 bp. The lengths between the end of microsatellites and the end BAC end sequences 15 were calculated by using the total lengths of sequence minus the stop positions of the microsatellites. Microsatellites were also excluded if the length was less than 50 bp after the microsatellites. All the remaining microsatellites were regarded as microsatellites with enough flanking sequences for the design of primers. The distinct BACs with microsatellites with sufficient flanking sequence were identified by using linux command uniq. The resulting unique set of BAC end sequences containing microsatellites were used to design primers using Msatfinder. Only a fraction of these so-called microsatellite-containing BAC end sequences with sufficient flanking sequences supported successful primer design as many flanking sequences contain sequences of low complexity that prohibit generation of PCR primers using Msatfinder. 2.3. Assessing the utility of BAC-anchored microsatellites for linkage mapping The usefulness of the identified microsatellites depends on their polymorphism. For genetic linkage mapping, their usefulness can be tested in the resource families. We have tested a fraction of the identified microsatellites in one of resource families, F 1 -2 x channel catfish-6. PCR primers stored in the primer.csv file generated by Msatfinder provide five pairs of primer sequences. The first pair of primers was selected and purchased from Sigma Genosys (The Woodlands, TX) if the G/C content was 40 % to 60%, and the PCR product length was 100-300 bp. In cases where first pair of primers had a very low G/C content, or the anticipated PCR products were long (>300 bp), the second to the fifth pair of primers was evaluated until the criteria were met. The PCR 16 condition was optimized by FASTPCR according to the primers generated from Msatfinder. PCR amplification was conducted using a thermocycler (Eppendorf AG, Brinkmann Instruments, Inc., Westbury, New York) using the following amplification profiles: 1X PCR buffer, 2 mM MgCl 2 , 0.2 mM each of dNTPs, 4 ng upper PCR primer, 6 ng lower PCR primer, 1 pmol labeled primer, 0.25 units of JumpStart Taq polymerase (Sigma, St. Louis, MO), 20 ng genomic DNA, in a total reaction volume of 5 ?l. After an initial incubation at 94?C for 90 seconds, PCR was carried out at 94?C for 30 seconds, 30 seconds at the appropriate annealing temperature specific with primer sets (50?C to 60?C, see database for details), 72?C for 45 seconds, for 35 cycles. Upon the completion of PCR, the reaction was incubated at 72?C for an additional 10 min. The PCR products were analyzed on 7% sequencing gels using a LI-COR automated DNA sequencer. After gel electrophoresis, the positions of both alleles from the male and the female were determined, and their segregation was confirmed by genotyping eight fish of the mapping population. Upon genotype calling and determination of allele segregation, polymorphism in the resource family was determined. 17 2.4. Development of a database for BAC end sequences and their associated microsatellites A database for BAC end sequences and microsatellites were developed based on the Apache/Mysql/PHP/CGI platform. The microsatellite information was categorized to six Excel sheets for the database. Sheet one contains the ID information including the GenBank BAC end sequence accession ID and Microsatellite ID. Sheet two contains the BAC end sequence information including GenBank BAC end sequence accession ID, AU ID (BAC clone name) and BAC end sequence. Sheet three contains the microsatellite information including Microsatellite ID, motifs, numbers of repeats, total length, position, and primer ID (if available for design primers). Sheet four contains the PCR primer information including primer ID, upper primer sequences and lower primer sequences. Sheet five contains the PCR condition information including primer ID, annealing temperature, cycles, and other reaction information. Sheet six contains polymorphism information including microsatellite ID, polymorphism information, and linkage group ID (will be amended when available). The primary key microsatellite ID, the foreign keys accession ID and primer ID were used to establish the relationship among the sheets in the database. This database will be available for query of the microsatellite information, including the BAC end sequences, motif type and numbers, primers, PCR conditions, and polymorphism information related to the linkage group. 18 3. Results 3.1. Identification of microsatellites in BAC end sequences A total of 20,366 BAC end sequences we previously generated (Xu et al., 2006) was used as the source for the identification of BAC-anchored microsatellites. A total of 5,553 microsatellites (including multiple microsatellites per BAC end sequence) was identified from the 20,366 BAC end sequences. Of these, some BAC end sequences harbor more than one microsatellite; also as both BAC ends were sequenced, some BACs harbor microsatellites in both BAC end sequences. For the purpose of linkage mapping, we are interested in mapping only one microsatellite per BAC. A total of 3,652 distinct BAC clones were found to harbor at least one microsatellite. In order to be useful for mapping, the microsatellite-containing BAC end sequences must have sufficient flanking sequences for the design of PCR primers. Analysis using Msatfinder revealed that 605 BAC end sequences harbored microsatellites at the very beginning of the BAC end sequences, and 1,296 BAC end sequences harbored microsatellites at the end of the BAC end sequences (Table 1). These will not be useful for testing as markers unless additional sequencing is conducted. After eliminating these, a total of 2,744 distinct BAC end anchored microsatellites had sufficient flanking sequences for primer design (Table 1, for details, Table 4). 3.2. Characterization of the BAC-anchored microsatellites The majority of the microsatellites identified from the BAC end sequences were dinucleotide repeats (63.5%), while the tri- and tetra-nucleotide repeats accounted for 19 22.0% and 14.5%, respectively (Figure 1). Of the dinucleotide repeats, the most abundant types were AC (27.1%), AT (27.0%), and GT (23.6%), while AG (14.0%) and CT (8.1%) were much lower; and the CG type was very rare (0.06%) (Figure 2). As the BAC end sequences were obtained from both strands of the catfish DNA and the true orientation of the BAC end sequences were unknown, the four distinct dinucleotide repeat types are AC/GT: 50.7%, AG/CT: 22.1%, AT/TA: 27.0%, and CG/GC: 0.06%. Clearly, the AC/GT type of dinucleotide repeats were the most abundant type in the catfish genome, accounting for over 50% of all dinucleotide repeats. The tri-nucleotide repeats are uneven in distribution, with ATT (35.8%) and AAT (27.8%) being most abundant (Figure 3). These two types of tri-nucleotide repeats accounted for over 63.6% of all tri-nucleotide repeats. It is apparent that all A/T-rich repeat types were more abundant than G/C-rich repeat types. For instance, after the most abundant ATT and AAT (both are 100% A/T repeats), all tri-nucleotide repeats with two of their three bases of the repeats being A or T had a representation of at least 2.6%, whereas all G/C-rich tri-nucleotide repeats with two of their three bases being G or C were all below 0.6% of the tri-nucleotide repeats with the exception of AGG (1.5%) (Figure 3). 20 Figure 1. Percentage of di-, tri-, and tetra-nucleotide repeats identified from BAC end sequences of catfish. 63.5, 63% 22, 22% 14.5, 15% Dinucleotide Trinucleotide Tetranucleotide 21 Figure 2. Distribution of various types of dinucleotide repeats identified from BAC end sequences of catfish. Note the low representation of G/C-rich types. 0 0.05 0.1 0.15 0.2 0.25 0.3 cg ac ag at ct gt Di-nucleotide repeats % of di - nucl e ot i d e r e peat s 22 Figure 3. Distribution of various types of trileotide repeats identified from BAC end sequences of catfish. Note that A/T-rich types are highly abundant. 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 aac aag acc aat acg act agg agt att ccg cct cgg cgt ctt ggt gtt Tri-nucleotide repeats % o f tr in u c le o t id e r e p e a t s 23 Very similar to the situation of the trinucleotide repeats, the distribution of tetranucleotide repeats was not even (Figure 4). They were most abundant with AAAT (18.9%) and TTTA (16.4%). In general, it was also true that tetra-nucleotide repeats with greater A/T had a greater representation. For instance, tetranucleotide repeats with at least three bases being A or T accounted for almost 80% of all tetranucleotide repeats with AAAG (13%), AAGT (10%), AAAC (6.7%), GTTT (5.7%), CTTT (2.8%), AACT (2.6%), AGTT (1.7%) among the most abundant types. The only exception appeared to be AATT which accounted for only 1.3% of all tetranucleotide repeats. G/C-rich tetranucleotide repeats were rare with many types not detected at all (Figure 4). Microsatellites with repeats longer than five bases were found rare in the catfish genome and therefore they were not characterized. 3.3. Assessment of the utility of the BAC-anchored microsatellites for linkage mapping In order to be mapped on the genetic linkage map, microsatellites must be polymorphic in the resource families used for genetic linkage mapping. To assess the proportion of the BAC-anchored microsatellites useful for linkage mapping, PCR analysis was conducted using the parents of the mapping population. A total of 500 pairs of primers were ordered for testing. As shown in Table 2, of the 500 microsatellites tested, 211 (42.2%) were polymorphic within the (F 1 2 x Channel 6) resource family. 24 Figure 4. Distribution of various types of tetranucleotide repeats identified from BAC end sequences of catfish. Note that A/T-rich types are highly abundant. 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 a aac a aag a aat a acg a act aa gg aa g t aa t t a ccc a ccg a cct ac g t ac t t aggg a ggt ag t t a ttt c ccg cc g t c ggt cg t t g ggt c ttt gg t t g ttt Tetranucleotide repeats % of t e t r an ucl e ot i d e r e p eat s 25 It seems that tri-nucleotide microsatellites produced the highest percentage of polymorphism within the resource family. Of the 349 tested dinucleotide repeats, 133 (38.1%) were polymorphic; of the 80 tested tri-nucleotide microsatellites, 46 (57.5%) were polymorphic; and of the 71 tested tetra-nucleotide microsatellites, 33 (46.5%) were polymorphic in the resource family (Figure 5). While polymorphic levels were similar (and also in some cases the numbers were too small to make a meaningful assessment, Table 3) among various types of trinucleotide and tetranucleotide microsatellites, it appeared that CT (48.5%) and AG (43.9%) types of dinucleotide repeats were most polymorphic, whereas the AT type (20.9%) of dinucleotide repeats were least polymorphic in the resource family (Figure 6). 3.4. Database construction for the BAC-anchored microsatellites A web-based searchable database was constructed for the BAC end sequences, and their associated microsatellites. Information included in the database included BAC clone name, BAC end sequences, GenBank accession number, microsatellite motifs and location, microsatellite primer name, primer sequences, and PCR conditions. 26 Figure 5. Comparison of polymorphic rates of di-, tri-, and tetra-nucleotide repeats within the resource family. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Dinucleotide Trinucleotide Tetranucleotide Microsatellite types % p o l y mo rp h i c 27 Figure 6. Comparison of polymorphic rates of various dinucleotide repeats within the resource family. 0 0.1 0.2 0.3 0.4 0.5 0.6 AC AG AT CT TG Dinucleotide microsatellite types % po l y m or phi c 28 4. Discussion In this work, a large number of microsatellites were identified from BAC end sequences of channel catfish. These microsatellites represent a major fraction of microsatellites in catfish identified to date. These microsatellites will be significant not only as potential polymorphic markers for genetic mapping, but also as the marker resource for integration of genetic linkage and physical maps as they were developed from BAC clones that are already fingerprinted for the construction of a physical map (Xu et al., in review). Mapping of these BAC end-derived microsatellites will not only add additional markers on the linkage map thereby increasing map resolution, but may also improve the coverage of the linkage map because BAC end sequences are more randomly distributed along the genome than are gene-containing regions. Earlier efforts in microsatellite marker development in aquaculture species were accomplished by the construction of microsatellite-enriched libraries (e.g., Liu et al., 1999, Carleton et al., 2002; Coulibaly et al., 2005). However, recently, it has been shown that the identification of microsatellites through data mining is a very effective way for marker development (Serapion et al., 2004; Ju et al., 2005; Schwenkenbecher and Kaplan, 2007, Garnica et al., 2006; Blenda et al., 2006; Perez et al., 2005). In many instances, microsatellite markers were identified using EST resources. Here we demonstrate that data mining is also very effective using BAC end sequences for the purpose of identifying microsatellite markers. The limiting factor is the availability of BAC end sequences. In catfish, we previously generated 20,366 BAC end sequences. In order to integrate the linkage and physical maps to the fullest extent, microsatellite markers need 29 to be developed from as many BAC clones as possible among those BACs that have been fingerprinted for the construction of the physical map (Xu et al., in review). Efforts for the sequencing of additional BAC end sequences are ongoing in our laboratory. To fully integrate the physical map with the genetic linkage map, multiple polymorphic markers are needed from a single contig to both integrate and orient the linkage map with physical map. Analysis of the utility of the BAC anchored microsatellites for linkage mapping was determined by testing the polymorphic status in the resource family used in catfish linkage mapping. In spite of the large numbers of microsatellites identified from BAC end sequences, their utility for linkage mapping depends on the nature of flanking sequences to support PCR primer design, the amplifiability of the designed microsatellite primers for the generation of PCR products with high fidelity, and the polymorphism of the microsatellites within the resource family. Clearly, the largest loss of the number of microsatellites useful for linkage mapping resulted from the flanking sequences with low sequence complexities. Of the 2,744 distinct BAC harboring sufficient flanking sequences as defined by the presence of at least 50 bp flanking sequences on either side of the microsatellites, only 1,100 (40%) supported primer design using Msatfinder. The next major reduction of useful microsatellite for linkage mapping resulted from the lack of PCR products or PCR products without fidelity, or the lack of polymorphism within the resource family. Of the 500 pairs of PCR primers tested, 211 microsatellites (42.2%) were polymorphic in the resource family. It appeared that the trinucleotide (57.5%) and tetranucleotide repeats (46.5%) had a higher level of polymorphism in the resource family than the dinucleotide repeats (38.1%). Among dinucleotide repeats, it appeared 30 that the AT repeats had the lowest polymorphic rate in the resource family tested. Such information concerning repeat types and polymorphic rates will allow us in the future to pick the microsatellites most likely to be polymorphic as our BAC end sequence resource expands. Obviously, the tri-, and tetra-nucleotide repeats are favored because of their greater polymorphic rates and much reduced problems in stutter bands, a common problem for dinucleotide repeats. The tested polymorphic microsatellites are ready for mapping. Based on this polymorphic rate, an estimated 460 polymorphic microsatellites will be available for linkage mapping from the present set of BAC end sequences. In spite of their significance for linkage mapping and for the integration of the linkage and physical maps, many more BAC-anchored markers are required for full integration of linkage and physical maps. Catfish has 29 pairs of chromosomes, the estimated 460 markers will provide approximately 16 markers per chromosome, or approximately one marker per 8 cM. It is obvious that many more markers are needed to bring a greater level of map resolution for detailed analysis of aquaculture traits. From the perspective of physical mapping, the current assembly of the BAC contig-based physical map has over 3000 contigs. Therefore, just one marker per contig requires 3000 polymorphic markers, and multiple markers per contig are needed to orient the contigs on linkage maps. Clearly, more BAC ends should be sequenced. Additional efforts are ongoing in our laboratory in BAC end sequencing, and in refinement of the physical map to bring the number of contigs to a smaller scale. The distribution of microsatellites in the catfish genome is highly biased toward A/T-richness. This is particularly true for tri- and tetra-nucleotide repeats as almost all 31 microsatellites with a higher A/T have a larger representation than the G/C-rich microsatellites. This is probably due to the fact that the catfish genome is AT-rich, estimated to be 60.7% (Xu et al., 2006). Long term genome research requires establishment of various databases such that linkage information, BAC clones, their associated sequences and markers can be easily accessed and tracked. In this work, we have constructed a database presenting BAC end sequences, microsatellite location, microsatellite types, microsatellite primer location and sequences, PCR conditions, and polymorphic information in the resource families. This database can be amended upon generation of additional information related to linkage and physical maps. The microsatellites developed from BAC end sequences, along with this database, will provide a valuable resource for the integration of genetic linkage and physical maps in catfish. III. Conclusions My thesis project has four objectives: a) To identify microsatellites from BAC end sequences through data mining; b) To characterize microsatellites identified from the BAC end sequences concerning repeat types, microsatellite repeat numbers, location within the BAC end sequences, flanking sequences, and a distinct set of BACs containing microsatellites; c) To test polymorphism of BAC-derived microsatellites in our resource family used for the construction of the genetic linkage map by using PCR analysis and determination of their segregation among individuals of the resource family; and 32 d) To develop a database for the BAC-anchored microsatellites, making them a useful resource for the integration of the genetic linkage and physical maps. I have used the BAC end sequences generated in our laboratory (Xu et al., 2006), and mined for microsatellites using 20,366 BAC end sequences. I have identified a total of 2,744 distinct BACs harboring microsatellites, and thus the first objective was successfully reached. I have characterized the identified microsatellites. Over 60% of all microsatellites identified were dinucleotide repeats, of which the major types were AC and GT types. Of the tri-, and tetranucleotide repeats, the A/T-rich types were more abundant. I have assessed the utility of the microsatellites by testing their polymorphism in the resource family. Of the 500 pairs of tested primer pairs, 211 were polymorphic (42.2%). These polymorphic microsatellites will be useful for genetic linkage mapping. Mapping of these microsatellites will greatly enhance the map resolution. More importantly, once these microsatellites are mapped, many contigs will be anchored to the linkage map, allowing partial integration of the genetic linkage and physical maps. However, as the estimated number of polymorphic microsatellites is approximately 460 out of this batch of BAC end sequences, they are not enough to fully integrate the linkage map with physical map. This is because we have 3000 some contigs, and we need at least one microsatellite per contig to anchor the contigs to linkage maps. Moreover, multiple microsatellites per contig may be needed to orient the contigs. The limiting factor is the availability of BAC end sequences. I suggest that BAC end sequencing should be expanded and actually that is ongoing in our laboratory. Once more BAC 33 end sequences become available, similar work should be conducted to significantly increase the number of BAC-anchored microsatellites. A database has been constructed contained all useful information such as BAC end sequences, GenBank accession numbers, microsatellites, their location, motif types, microsatellite primers, and PCR conditions. 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Biological Bulletin 204, 327-338 46 Table 1 A summary of the microsatellites identified from BAC end sequences BAC end sequences 20,366 Microsatellites found 5,553 Microsatellites at the beginning of BES 605 Microsatellites at the end of BES 1,296 BES with microsatellites and enough flanking sequences for primer design 3,652 Distinct BAC clones harboring microsatellites with enough flanking sequences for primer design 2,744 47 Table 2 Assessing the utility of the BAC-anchored microsatellites for linkage mapping. *Number of distinct BAC end sequences with at least 50 bp flanking sequences both upstream and downstream of microsatellites. Quality flanking sequences were defined as sequences that support primer design using Msatfinder. Number of distinct BAC harboring microsatellites with sufficient flanking sequences for primer design* 2,744 Number of distinct BAC harboring microsatellites with quality flanking sequences allowing for primer design** 1,100 40.1% Primer pairs designed and purchased 500 45.5% Number of polymorphic microsatellites in resource family (F 1 -2 x Channel-6) 211 42.2% 48 Table 3 The number and polymorphism tested from various types of microsatellites Microsatellite types Number of microsatellite primer pairs tested Number of polymorphic microsatellites % polymorphic AC 129 51 39.5 AG 41 18 43.9 AT 43 9 20.9 TC 33 16 48.5 TG 103 39 37.9 Sub-total 349 133 38.1 AGG 7 3 42.9 AAT 27 14 51.9 AAC 3 1 33.3 ATC 2 2 100 CTG 1 0 0 GGA 1 ATG 7 4 57.1 ATT 27 18 66.7 GTT 1 0 0 TCC 1 1 100 TTC 1 100 TGG 1 1 100 GTT 1 100 Sub-total 80 46 57.5 AACA 8 3 37.5 AATA 13 7 53.8 TAAC 2 1 50 AATC 2 50 TAAT 1 0 0 GACA 1 ATCT 4 2 50 ATGG 5 0 0 TCCA 4 3 75 TATT 21 9 42.9 TGTT 6 4 66.7 TTTC 2 1 50 TGAA 2 2 100 Subtotal 71 33 46.5 Total 500 211 42.2 49 Table 4 Microsatellites identified from BAC end sequences. P indicates polymorphism, NP indicates no polymorphism, empty cells indicate no PCR products were generated with fidelity such that genotyping was not possible. BAC name Locus name Microsatellite position Motif Primer sequences (5? to 3?) Annealing temperature (?C) Polymorphism AU01010A1A06.f1 AUBES1073 350 (TA) 25 TGCTACTCTGTTGGTGCCAG GACACCAAAATGTGAAGGGTGTTCTC 50 AU01010A1E12.f1 AUBES1074 350 (AACA) 5 GTCCAGTGTTTGGTAGCCAC GACCAACCAACTTTGAACCACATTGC 50 AU01010A2A02.r1 AUBES1075 230 (AATA) 9 GTGCTCTGTTAGCTGGAGTG GACAAGCAAGCCTGGACCATGAC 55 P AU01010A2E09.r1 AUBES1076 610 AATA(5) AGCTACATAGCTGGGGAGTC GACCAGAACCACTGTGTCCACAG 50 AU01010A2G11.r1 AUBES1077 440 (AC) 31 TCTACTGCTGCCTGTGAACG GACGCTGAAACAGACTGTGGACAC 50 P AU01010B2H08.r1 AUBES1078 360 AATA(5) TGGGTTGTGTGATGTGGCTC GACGGAAAAGCTGTTTATACCTGCTGG 50 P AU01010B2A03.r1 AUBES1079 550 (ATG) 7 CGTTTCATTCCTCTTATGCCAGC GACGTTTCATACATGATCCAGGCCATC 55 P AU01010B2H10.r1 AUBES1080 310 (ATT) 9 CACCTCCATGCCACCAGAGG GACCTGAAGCACTTCGGTCAACTC 50 P AU01010B2A02.f1 AUBES1081 260 (ATT) 13 CATCAACTACAATATCAGCCGCAG GACTGGAGGCGACAGGCAGGTGG 50 P AU01010B2C03.f1 AUBES1082 280 (ATT) 15 TTGGGGCCTGTGGGGCTTGG GACAGAAGTGTTCAGCCTGTTGG 55 P AU01012A2A05.r1 AUBES1083 264 (GA) 28 ACAGGACGATGCTGGCAGTG GACTCGACACCAACATGACCGAC 55 P AU01012B1E03.r1 AUBES1084 590 (TTTG) 9 ACCATCGTGTATCGCGGACG GACACGGAGTTGCAGTCACCACG 55 P AU01012B2C01.f1 AUBES1085 347 (TTTA) 6 CGATCCTGTCCGGCGTTCTG GACAAACCCGGTGACACGACTGC 55 AU01018B1F11.r1 AUBES1086 280 (TG) 13 TTGCCAAGAACCCGTTGAGC GACTGGGAAGCATTTGGTTTGGTC 55 P AU01018B1F03.r1 AUBES1087 490 (ATA) 13 TCAAGTGCAGAAATTACTGCCAC GACACCTTCAAGGGTGCGAAGAG 50 AU01018B1D04.r1 AUBES1088 200 (AC) 19 ACTGAACCAGAGCAGAGTCC GACGGTACGTTCAATACTTCTGGCAC 55 P AU01018B2F12.r1 AUBES1089 490 (AT) 16 CTTATTTCCCCTACAGTGTGTGTG GACGTAGAACCCATCACCCTTTGG 50 AU01018B2C07.r1 AUBES1090 350 (CT) 13 GTGATGAGTCAATGCAACTCAGG GACACAGACGCATGACAGCTTCC 55 P AU01018B2B09.r1 AUBES1091 430 (TG) 18 ACGGTCCTACACACTCCAGG GACGTGTGACGAGTGGCTGAAGC 55 P AU01018B1E06.f1 AUBES1092 470 (TG) 11 TCATGGTTACAGGCTTGCAG GACCACAGGCTCACCGAAACTGG 55 P AU01010A1A12.f1 AUBES1093 570 (TG) 15 AGCCACATAAAAGCCTGTCC GACGGAGCACTAACACAGACACC 55 P AU01010A1E01.f1 AUBES1094 400 (TG) 18 AAGCCAACCCCAAAGCCTCG GACATCCCAGAGGAGAATGCTGC 55 AU01010A1G11.f1 AUBES1095 580 (TTTA) 5 ATCACGCACACCCCAAACAC GACTCCTCCCTGCCTGGCATGAG 55 P AU01010A2C11.r1 AUBES1096 280 (AC) 19 AATCTGCCACTGCTGTTGAG GACGTCAAGCACATGGCATGACC 55 P AU01010B1C06.r1 AUBES1097 220 (TG) 15 CTTCGGTCTTCTCGAAAGTGG GACGACAGTGCAGCGTAGTGGAG 55 P AU01010B1H11.r1 AUBES1098 660 (AG) 8 GGGGTGTGTGTGCGTTTAGG GACCCACCAAAAGTACACGATGCTC 55 AU01010B2B06.r1 AUBES1099 230 (GA) 29 AGTTGTTGGTCAGCGCCAGG GACACATCAGCCAGCCCTGTGTG 55 P AU01010B2H02.r1 AUBES1100 300 (ATAA) 4 GCTACATGCTGTGAAGCTCC GACGTTTTACTTTGAGTGTCGTGACTACC 50 AU01010B2A11.f1 AUBES1101 640 (AACA) 5 CCCACTGAATGTGTTGCTCG GACTAATCAGGCCCGGTTGCGTC 50 AU01012B2E08.f1 AUBES1102 394 (TG) 9 ACCTTGATAAGCACGTCAACG GACAATCTCACCGTGGCCTTGAG 55 AU01010A2A08.r1 AUBES1103 360 (TC) 17 GGAGTGAGCTGTGTGCCCTG GACTGGCAAAGTAGCACTGTGTC 50 AU01010B2C09.r1 AUBES1104 363 (ATAA) 9 CATTATGGCGGGTCATGTGC GACTTGCTTGGTAATAGTCAGCGTGTG 55 NP AU01012A2C01.r1 AUBES1105 375 (AACA) 6 TGTCAAAGGCACACACAACG GACCCTGCTGGACTGAGGGGCTC 50 P AU01012A2G03.f1 AUBES1106 690 (AG) 17 CCGTCAACGACAGCAACAGC 55 NP 50 GACCCGATACATGCTGGAGCCAC AU01012B1D03.r1 AUBES1107 510 (ATT) 19 AGGTCATTCTGGGGTCACTC GACTGGCTGAGTATCGGCTATGC 50 P AU01012B1C08.f1 AUBES1108 80 (AC) 29 GTCGCTGAGCAGCAGCTCTC GACCACGGTCAAAACCCTCTGTTC 50 AU01012B2A01.r1 AUBES1109 109 (TG) 12 AGAGGCATCCCAAGTGGCTG GACGTGGATTTCCTGTAACCGTGG 50 P AU01012B2D01.r1 AUBES1110 465 (AGA) 8 AAACTAGCACGCCAAGTAGC GACAGCTTTGTGTACGGTCGCTG 50 AU01012B2F02.r1 AUBES1111 385 (AC) 9 AGATAACGGAGAGCAGCCAC GACTCCTCCCGCAACTGCCGCTG 57 P AU01012B2F08.r1 AUBES1112 161 (GT) 16 CTTGGCACTTCACCCGCCAG GACACCTGGAGCTGCTCAGCGTG 57 P AU01012B2G07.r1 AUBES1113 386 (AT) 19 GCCATGTTCAGGTAACGTGG GACGCAAAGTGTCATTTCTTCAGTGTCG 50 AU01018A1C03.r1 AUBES1114 358 (TG) 20 AAACTTACCCTCGGCGTGTC GACCTCAGAGTGTTCCAAAGCCTG 55 AU01018A1F06.f1 AUBES1115 196 (ATC) 10 ATATGTGTATGTGGCGTGCAG GACAGGAGCAAATGCTCAAGGTG 50 P AU01018A2C01.r1 AUBES1116 380 (AC) 8 TGGTGTGTCCAGGGTTGTGC GACACAAACCGCACTTCACCGAG 57 P AU01018A2C02.r1 AUBES1117 109 (ATT) 15 GCCCAAACATACTGGCTACC GACAGGGCAATGAGCGTTTCCTG 50 P AU01018B1A07.f1 AUBES1118 350 (GAT) 6 AGTCTCACAGATAGTCCTGGTG GACAGTAAGTCAGTATGTAAGCTCCCAG 50 P AU01018B1E06.f1 AUBES1119 470 (TG) 11 TCATGGTTACAGGCTTGCAG GACCACAGGCTCACCGAAACTGG 50 P AU01018B2E07.f1 AUBES1120 510 (AC) 11 TAAGTGAGGGAGCCGGAATC GACCCATAACGCTTCCAGAGTGAC 55 P AU01007A1C11.f1 AUBES1142 350 (AC) 17 TCCCTAGTGCCTCGTGTGTG GACTGTAGACAGCAGCGAGCCTG AU01007A1H05.f1 AUBES1143 200 (GA) 15 CTTACACACACTAGCTTGCACC GACGTCTCCAGTGTATGTGAGCAC 55 AU01007A2D08.r1 AUBES1144 280 (TC) 9 ACATCAGCAAAGGCTTGACAG GACTCACAGCAACTTGCCAAAGAG 55 AU01007A2D11.f1 AUBES1145 150 (GA) 16 AGGAGGTCTGATGGTTGTGG GACGCTGATGTCCGATTGCCCAC 55 P AU01007B1F03.r1 AUBES1146 450 (GA) 12 AGTGGCTGCTCTGAGGCGTG GACTCAAAACCAAAAGCAGGTCAGAC 55 P AU01007B1C07.f1 AUBES1147 430 (TAAA) 9 TCAAGACAGGAGCCAACCTG GACTCCTTTAGCCTGGGCCAGAC 55 P AU01007B1H06.f1 AUBES1148 200 (TC) 13 CCCTCTTAAACGTGCGCGTG GACCATACACACACGGACACATCC AU01007B2B12.r1 AUBES1149 360 (TG) 11 GAGGCCCAGTACAACGTACC GACACAGCTACACACCCACAATG 55 AU01007B2E01.r1 AUBES1150 250 (TG) 12 ACATGCCCCTCTAGCACCAC GACAGCCATCTGTGTGTGGGGAC 55 AU01007B2E05.r1 AUBES1151 120 (TC) 22 GATTGTGAGGTAGGCACTGC GACACAGAGGTGACTCAGGGCTG 55 P AU01007B2H08.r1 AUBES1152 250 (TG) 8 GCCTCCATGTTGACGCACAC GACAGAGTCGTTACTGACCGCAC 55 NP AU01007B2A11.f1 AUBES1153 200 (TAC) 9 AAGGAGCTGTCCTGTTCACC GACGTTTATGGGTAACCCTGTCAAGG AU01007B2H03.f1 AUBES1154 120 (TTTA) 5 TCTAAGTCTTATACCTGGGGTTGTC GACTTCTGCTCCAGGGGTGCTTC AU01008A1B11.r1 AUBES1155 280 (ATG) 10 CATTGTCTGCCAACCGAAGC GACTCCACAAATGATCCGAAGTGC AU01008A1C06.r1 AUBES1156 200 (CA) 10 CGCGCTTTATTGTGAGCATGAC GACGAAGCCCTTTCTCAGGAACG AU01008A1F09.r1 AUBES1157 540 (TG) 9 TGGAGCCACTGTACTCGTGC GACACACACACTCAGCCTGTTGG 55 AU01008A1H05.r1 AUBES1158 580 (TG) 15 TAAGGAGTGACAGGCGGCAC GACGATTACGGCGTGATTGGCAC 55 AU01008A1H07.r1 AUBES1159 170 (GA) 17 ACATCATGCACATGCAGAGC GACCAGGTAAACGACGTGGTCTG 55 AU01008A1A06.f1 AUBES1160 390 (TTTA) 4 ACTGTGCCATACGTCTCTCTG GACAGAACAGTCATTGCAGGTGTC 55 P AU01008A1G06.f1 AUBES1161 340 (AC) 8 CAGGTCAGCAAGGGGGTTCG GACTCTGATAAGAGCAGGGGTGAC 55 AU01008A1H03.f1 AUBES1162 95 (CT) 12 TGTTCATGGCTTGCGGTCAG GACGGGTGTATCCCACTGCTCAG AU01008A2A10.r1 AUBES1163 450 (ATGG) 5 TGGGACGATGCACGTTCCTG GACGTACACCCTGAAGGGCTTTG 55 AU01008A2B06.r1 AUBES1164 170 (ATT) 8 CTGGGTTTAGGGGTGGAAGC GACTGATGGCCCGTTGTGGTGTG 55 P AU01008A2B11.r1 AUBES1165 420 (AAT) 12 AATGGCAGAAGGTTTCCACC GACTCCGAACCTACGAGACAAGC AU01008A2E03.r1 AUBES1166 310 (AC) 8 CTGGGATTACTTGCTCACTGG GACATACAGGGCTCGCTTCACAG 55 AU01008A2E08.r1 AUBES1167 400 (AAAT) 5 GGGCATTAACATTTGACCGAGG GACGCCTCAACAATTTGGTGTGG 55 AU01008A2F09.r1 AUBES1168 180 (TG) 19 CAGAGCCAAAGTTCCCTCTG GACTTTCTTTTGAGAGTCCAGTGTGC 55 P AU01008A2G03.r1 AUBES1169 240 (TCCA) 5 TGCCTTGTACCCGATGCTCC GACACCTCACACCTCGGAGACAG 55 AU01008A2G06.r1 AUBES1170 150 (TATT) 5 CCTATAAGAAGTTCACTACAGCCTGTC GACCTGGAAGCAATGTGTTGTGC 50 AU01008A2H03.r1 AUBES1171 250 (AC) 8 GTCAAAGATACAGTGGAACTGAGC GACACATAATCTAGGTTCGCCTCTGG 50 AU01008A2D03.f1 AUBES1172 380 (TC) 10 AGTGTTTCCTTGGCGGTGTC GACTCCCTGTCCACCCTCCATCC 55 51 AU01008A2H08.f1 AUBES1173 210 (CA) 12 GATTGTGAAGCAGTGGCAGC GACAGCGGGAGTAACGTGTTGTC 55 AU01008B1B03.r1 AUBES1174 55 (ACAA) 13 CGTGTTTGTTTTCGTGCAATGC GACATGTGGGCTAACTGCACTGG 55 AU01008B1G01.r1 AUBES1175 550 (GA) 12 TAAGGGGTGTGTGTGCGTTC GACAACTCCTGGAGGGCATCCTC 55 AU01008B1A06.f1 AUBES1176 370 (TG) 18 AACACACTCACAGAAGCCAC GACAATAGAGGGGGCATGACCTC 55 AU01011A1A02.f1 AUBES1177 383 (AC) 13 GGACATCCTTGAAGGACGTG GACTGGTTACGCTGCTCTGATGC 55 P AU01011A1B12.f1 AUBES1178 71 (CA) 14 TCCTGGTCAGGGTTGTGGTG GACTCCGGCCAGCGCCATAGACG 55 P AU01011A2C01.r1 AUBES1179 218 (AC) 17 ACGTGCATTAGTGGCCCCTG GACACTCCATTGGTTTGAGGCAC 55 P AU01011A2F02.r1 AUBES1180 362 (TTTG) 7 GGACACATATCATGGCTTGTGC GACACCATGTTGAACTGCTCTTCAC 55 AU01011A2F09.f1 AUBES1181 265 (AC) 20 ACTGAAAGCATCTGTTGCACG GACGACCTTGATAAGGCGACACTC 55 AU01011B1A10.r1 AUBES1182 175 (TG) 10 AGGCACGCAAAGGCCCAACC GACTTCGGTACGGACGCAGTGTC 55 P AU01011B1C04.r1 AUBES1183 222 (TG) 9 TGACGTATGGTTGGCAACAC GACTTATTTGGGGAACGGCAGTG 55 AU01011B1E02.r1 AUBES1184 262 (TG) 10 CACTACACACTTGACCATCGAAC GACCTCCAGAACTGAAGCAGCAC 55 NP AU01011B1H04.r1 AUBES1185 313 (CA) 27 TCATGCCATTTAGCGGCCTG GACTGTTTACCGGCTGTGCGAGC 55 P AU01011B2C12.f1 AUBES1186 377 (AC) 9 AGACCCCAGCCAAGCTGTCC GACGATTAGGGCACTGAAGTCACG 55 AU01021A1C09.r1 AUBES1187 62 (TG) 10 TCCCACACAGCAGCCTCCAC GACTGCAAGCTATCGCACACCAC 55 AU01021A2F02.r1 AUBES1188 479 (AC) 33 CGAGTCCTTTAGAGGCCCAG GACCTCTCACTGTCCTCCACTGC 55 NP AU01021A2C05.f1 AUBES1189 70 (AAT) 6 GTGTATTTAATCCTGGGGTGAGTTG GACCGAAGTCCATAGGGGTTTCC 55 AU01021A2F03.f1 AUBES1190 145 (CA) 9 AGCAAAGTCATGCCTGCATG GACGACAAACGTGTGCAAGTTGG 55 AU01021A2F07.f1 AUBES1191 438 (TC) 20 ACTCCAGCAAAACATGCAGC GACAATTCACTCACACAGCTCACAC 55 AU01007A2E08.f1 AUBES1225 100 (AC) 15 ATTGTGATAGTGTGCTGTGGTG GACTCCTGAGCAAACTGAGCCTG 50 AU01008B1D04.f1 AUBES1226 400 (GA) 29 AGGGAGAGCGAGTCGCTGTG GACACCTGCTGCTGAGGCTGACC 55 P AU01028B1F11.f1 AUBES1227 170 (CA) 8 GCTCCCAATAGTAAGCCTAACTGG GACAGGGCACAGGCAAGCCACAC 55 P AU01023A1G09.f1 AUBES1228 170 (AAAT) 6 ACAGCCTGAAGACCCGTGAG GACACTCAGAACCTACAGCCAACTC 50 AU01023B1G08.r1 AUBES1229 120 (AG) 19 AACCCATCAGACACGCTCAC GACCTGGAGACAGCGCGAGGGAG 50 AU01022B2E01.f1 AUBES1230 215 (TG) 21 CCAACAGTCATCTGTCTGAGC GACTGTGCTCCCGTGGACCTCAG 55 P AU01025B1A10.f1 AUBES1231 135 (AC) 21 GTCGCATCATTTTGATTGCAGC GACGGACGGGCTCCTGTTGGACG 57 P AU01018B1F11.r1 AUBES1232 185 (TG) 13 TTGCCAAGAACCCGTTGAGC GACTGGGAAGCATTTGGTTTGGTC 50 AU01022B2B07.f1 AUBES1233 135 (AC) 16 ATGGCTATGGGACTAGGTGC GACACAAGCACATACACACGAGC 55 AU01032B1E03.r1 AUBES1234 225 (TG) 18 GGTCAGACATATTCCTCCAAAGC GACTCGATTATCGGTATCGGCTGAG 50 AU01036A1G04.f1 AUBES1243 70 (AT) 10 CGGCAAGTCGGGCGAGTTTC GACAGCATAAACAAGTAGCAGACAGC 55 P AU01036B1G11.r1 AUBES1244 220 (AC) 11 TCCTCCTCAGCAGGGGTGAG GACCAGAGGCAATGATGTGGTCC 55 P AU01014B2F05.r1 AUBES1258 330 (AC) 28 AGAAAGCAGCTTGCAGATGC GACGCACAAAAGTTCAGGCCATG 50 AU01001B1F04.r1 AUBES1259 125 (GA) 15 TTTGTGTGGTGCTATGCTGC GACGGGGTTATCAGAGTGGTCCTG 55 AU01010A2A04.f1 AUBES1260 70 (TAA) 18 CATGGGAGTGTGTGCATGTG GACGTCTGCCTCTGATGGAGTCG 50 AU01001B2A07.r1 AUBES1261 258 (AC) 21 AGAAAGCAGCTTGCAGATGC GACGCACAAAAGTTCAGGCCATG 50 AU01008B2F01.f1 AUBES1262 460 (TA) 8 AGTCCTAACTGATATTCAGTCCAGG GACCGGCTCGAAGGCCAACATCC 50 AU01033A2D02.f1 AUBES1263 535 (GT) 14 CTGACCTGAGCACTGGTGTG GACAGACGTATGGGCGAGATCAG 57 P AU01024B2C11.f1 AUBES1264 450 (ATG) 10 AAGGCAGCAAGGTGAGAAGC GACCGGCTCAGGTCGCTCATACG 57 P AU01033A2D11.f1 AUBES1265 75 (GAC) 6 ACACACGGAGGGGTCAGAGG GACTGTACGTTGGCTCGTTGACC 57 P AU01030B2E04.f1 AUBES1266 330 (TGA) 15 TAACAGCCGCTCCTGCATCG GACGCTATTATGTGTCTACTGCCAATCC 50 AU01001B2H05.r1 AUBES1267 479 (TG) 9 CTCCGCCGTTCAGTGCGAGG GACTGCCAACATAAAGAGAAGCTGG 55 P AU01011B2H06.r1 AUBES1268 480 (AGC) 6 ACTCCGCCGTTCAGTGCGAG GACTGCCAACATAAAGAGAAGCTGG 50 P AU01010B2G07.f1 AUBES1269 500 (AT) 10 ATGGTGATGGAGCCTGAGAC GACAGTCAAAGGCTTTTCGTCGTC 50 AU01030B1D09.r1 AUBES1270 225 (GATG) 5 CAGCCACTCCTCGGTGTGTG GACCAGGTCGCTTGTGGGATGAG 50 P AU01034B1D08.f1 AUBES1271 210 (AC) 28 GTTTCGCCTTTGACTGCGTC GACTGCCCTGTGGAGGTTGTTGG 55 AU01025A2E09.f1 AUBES1272 295 (AAT) 12 TCCTCATCTGGGTGACAACG 52 GACTAGGGTCCACAGCCAGCCTG AU01028B1C09.r1 AUBES1273 135 (CT) 8 TGTGAGAACAGTGACCGTGC GACTCGTAGATGGAGGCTCCGAG 57 AU01007B1B11.f1 AUBES1274 183 (CA) 30 GAAAGAAGCAGAACGAGAGAGG GACGTGGTGAGTTTCACTGACTCC 55 P AU01001B1D04.r1 AUBES1275 90 (AAT) 7 TCGTAGTGATCCACCACCTG GACCTCTAACTAACCAACCCACAGC 50 AU01008B1A06.f1 AUBES1276 412 (AC) 14 AACACACTCACAGAAGCCAC GACTGAAAGAGGCATGGGCTGTG 55 AU01014A1E03.r1 AUBES1277 133 (AATA) 5 GAATTTACGGGAGGCCGCTG GACACGGAGACTCCATGCTGAGC 57 P AU01028B1B05.f1 AUBES1278 265 (GT) 8 TCAACCCTGCAACGTCACAC GACTGTCGTTCAGAAAGCCGCTG 55 AU01022B1G11.f1 AUBES1279 305 (TCTA) 6 TCGTGAGCAGAACCCTGGAC GACCATGCAGCACATGGCCGTGG 55 AU01033B1A02.f1 AUBES1280 485 (AAT) 11 CTTGCCACTCCACCCGTTAG GACGCGTTTGAGTTCTCCAAACACC 50 NP AU01019A2C04.r1 AUBES1281 515 (AAT) 12 ACCATAATCTTGCCACAAGCAG GACTGGAGAGAGTCAACACGCAG 50 P AU01002A1H01.f1 AUBES1282 115 (GGA) 7 CTGTGACTCCCTCAGACTGC GACTCATTCTGCCACCTGCTGAC 50 P AU01006B1B04.f1 AUBES1283 314 (AC) 8 TTGGAGGACTGTTGGGTCAG GACACAAAACGCAGTGGGGTCTG 50 P AU01011A1D09.f1 AUBES1284 116 (TTTA) 6 CTGAGGTCTTCAACTGCCAG GACTCAGAACCAAACAGATGCAGC 55 AU01023A2H05.r1 AUBES1285 175 (AT) 8 GTTCAGTGAGATGTCGCCTG GACCCTATCCCGTACAGGTACACG 50 P AU01033B1D05.r1 AUBES1286 210 (AG) 11 GAATGGTATCCTCGCCAAGC GACGGACATTCTTCAGCAGCCAG 50 P AU01028A1C10.f1 AUBES1287 270 (TC) 19 TTGGGTCAGTTGACGCCTGG GACGGGCGGATCAGAACCGTCTC 50 P AU01020B1A04.f1 AUBES1288 72 (TAAC) 5 ACTAGAACTCTGGCCTAGCAC GACCTTTCAGTGAGGGCGTTTCC 50 AU01019A2A03.r1 AUBES1289 495 (TG) 18 AGCTGCAAAACCTACAGCAG GACTAACGGCACATCCCCAAGTG 50 AU01022B2A03.f1 AUBES1290 404 (CCA) 7 GTGTAAATGTCCAAACATGCACG GACACAAGTGGGAAACACTGTGG 50 AU01026B1E04.r1 AUBES1291 319 (GA) 9 CCCACAGCAGCCAGATGGAC GACTGGCAGGGAAACCCAAGCAC 55 AU01029A2E09.f1 AUBES1292 298 (GA) 13 CACCCAGAACTTGCACACAC GACTGCTGGTAGGACTGGTTTGG 55 AU01002B1A01.f1 AUBES1293 422 (TA) 11 TGGTCATGTGACTGTGATGC GACCTGAAGCAGAGTAGCCAACAC 50 NP AU01011B2D09.r1 AUBES1294 175 (AC) 9 CTGTTGCACCACTAGGGAGC GACTCGCAACGACTGCACTGGAC 55 AU01003A1B02.r1 AUBES1295 165 (GAA) 9 GTCCGTCTACGAACAGGGTG GACTGTGGGTTCTCTCGCATCAG 55 NP AU01025A1D09.f1 AUBES1296 335 (ATAG) 4 CAGACAGTGAACAGGAACTTGC GACACAACAGCACAGTCTGCCAG 55 AU01005B2H04.r1 AUBES1297 412 (TC) 11 TCTTCCGTCTCTCCGCACAG GACCTGCGAGGACTACGGCTCTG 55 AU01021A2F07.f1 AUBES1298 285 (TG) 19 ACTCCAGCAAAACATGCAGC GACAATTCACTCACACAGCTCACAC 50 P AU01021B2E09.f1 AUBES1299 214 (TC) 11 TCCAAAACCATCCGCGACAG GACAGCAGTTTAGACATGGCTGC 55 P AU01021B2H07.f1 AUBES1300 280 (AAAC) 5 CTATGTCAAGACTATGGCGATGTTG GACCTATGTTCAGATGACCCATGAAGG 55 P AU01006A2B03.f1 AUBES1301 435 (TA) 35 GCAGTCATCCAGAGTCCCAG GACTCCTTCACTGCTGTCTGAGC 55 P AU01010A2G11.r1 AUBES1302 330 (AC) 6 TCTACTGCTGCCTGTGAACG GACGCTGAAACAGACTGTGGACAC 55 AU01018A1E03.r1 AUBES1303 490 (TG) 31 GAACAAGTGCTGCACGGAGC GACAGTTCCCTGGCTCGGTTTGG 55 AU01007A2H08.f1 AUBES1304 290 (TC) 9 ACACAACATGGACAACGAGTC GACTCAGCTCCGTCACCTCCGAG 55 AU01003A1D11.f1 AUBES1305 430 (CA) 16 AACACAGCCTGCCTCTCATC GACGTAGGCGGTCTGCCTCTCAG 50 P AU01019B2H05.r1 AUBES1306 130 (AC) 16 TCAGCTCCAGCGCAACGAGG GACCACTCCCGGAATACACGCAG 55 P AU01021A2F02.r1 AUBES1307 440 (GA) 8 CACAACATGGACAACGAGTCC GACGTCCTCCACTGCTGTCTGTG 50 NP AU01028A1E08.r1 AUBES1319 185 (CA) 12 AGAGAAACCCTAAACTCACAGTCC GACGTGTTTTCTGAGCCAGGAGG 50 P AU01028A1C09.f1 AUBES1320 210 (TG) 16 CACACTGACGTACATACGTGC GACGTTTGGTCAGGGCTGATGTC 55 P AU01009B1D04.r1 AUBES1321 220 (TG) 13 AGGTAACAGCATCCACCCAG GACTCGGAACCTTCGAGTTCACG 50 P AU01023A1C05.r1 AUBES1322 365 (TC) 20 CTCAGAGTACACACAACCATCG GACGGGAGATACGTTCAGTCCGAC 50 P AU01023B2F01.f1 AUBES1323 413 (TG) 22 CCAGTGTTTTCAGCCATGTGAG GACATGACCCTCTGCGGAACCAG 55 P AU01031A2H01.r1 AUBES1324 306 (CA) 29 AAGTCTCTGGTGTCGTGTGC GACGGTGAGTTCCTAATGCTCGTG 55 AU01027A2B01.f1 AUBES1325 110 (AC) 30 AAGAATCACAGCCCAGATGC GACGTTTAGTCGCATACTGTTGCTCAC 50 P AU01004A2E04.f1 AUBES1326 430 (AC) 30 TTAGGATCGGTTAGCCGCAG GACTCAAGGCCATTTCTGCACTG 50 P AU01031A1C08.f1 AUBES1327 373 (TG) 19 CTCGCATCAGCACCATCTCG GACAGGGTCCTCCAACAGGCTCG 55 AU01022A1H01.f1 AUBES1328 229 (AC) 23 AGGTACTCCGACCTCCACAG GACACACAGAACGAGACAGGTGAG 50 P 53 AU01014A2F08.r1 AUBES1329 75 (TG) 19 TTGGCAGGACAGGTCAGTCC GACAACTGTAGCGCACGCTGTTG 50 AU01033A2G04.f1 AUBES1330 300 (AC) 10 CTTACCCATGTATCATTGGGACC GACAGCCTATCCCAGGGGACTTG 50 P AU01004A2H08.r1 AUBES1331 480 (AC) 30 TGCACAGTTAAATCATCACCAGC GACAGCATCAGCATGGACCTCAG 50 P AU01022A1D08.f1 AUBES1332 236 (GA) 19 CAGATACAAATAGGAGGCACACG GACGTAAAGTGTCCCTGAGCTGTG 55 P AU01014B1H11.r1 AUBES1333 111 (CA) 12 GGATTGTACGTTCTGCTTGACG GACTTGTGAGGGTCCTCATGCTC 55 P AU01002B2G03.f1 AUBES1334 103 (CA) 13 GTTCACGAGGACGTGGGTCG GACCCCAAGTGCCTGAGGCGAGC 55 P AU01014B2G09.r1 AUBES1335 203 (TG) 11 GTGTTGTACGTTTGGGCTAGAG GACAGGTCCTGACTATGTGCTGAG 55 AU01030B1A07.r1 AUBES1336 397 (TAA) 11 AGTCTCCCAGCACCGGCACG GACCACGCGCTCGAACCCAGAGC 55 AU01031A1H06.f1 AUBES1337 140 (AC) 8 TGAAGGGTGAATGAATGGAGC GACTGTATGTGGGACCACTGTCC 55 AU01025A1E11.r1 AUBES1338 295 (AC) 10 GACCACATGACGGCTTCC GACTTTCCAAGCCCGCTGAG 55 P AU01029A1C02.f1 AUBES1339 278 (CT) 23 TCTCCACATCTGACACCTGAC GACAGTGGTAGATCACCTTGGGTC 50 AU01028B1B06.f1 AUBES1340 205 (GA) 21 GTAGGCAGCGTTCCAGC GACTGCTGGTAGGCCCAGTG 55 AU01019A2B04.r1 AUBES1341 295 (TG) 9 GATTTCACATTTTCTGGCAGTGC GACCAGTAAAACACAGATGGTGTCTGAC 50 AU01006A2H12.f1 AUBES1342 140 (TAA) 16 AGGGCATCCACAGCTTCAGG GACTGAGAGCCCAGGTGTCTGTC 55 AU01006B2C09.r1 AUBES1343 372 (TCTT) 6 AACATGGCCCTGTGGTCAGG GACCAGCATAGGCGTCTGGCAGG 55 AU01010B2D02.f1 AUBES1344 206 (GA) 8 CATGACTTTTGCAGGTCCTCC GACGTGGTCATCTGTGGTGTCTG 50 NP AU01002B1E01.f1 AUBES1345 365 (TTA) 11 TGGATCAATCTCAATCAGGTCAGG GACGCTCATTCACAGGGACTTCAC 50 P AU01011B1H04.r1 AUBES1346 291 (GA) 14 TGCCATTTAGCGGCCTG GACTTTACCGGCTGTGCGAG 50 AU01011A2F09.f1 AUBES1347 283 (GAA) 6 AGCATCTGTTGCACGCTG GACAGCAAGACTGGGGTGC 50 AU01006A1C09.r1 AUBES1348 119 (TG) 9 GCGCAAGTTTAAGGATGTGTGC GACCCCATTCACCTCGACATGG 50 AU01013A1B03.f1 AUBES1349 308 (AC) 13 TGTATTGGCACCCCTTTCC GACGTCCTTCCCCGCTCTG 50 AU01014B1E02.r1 AUBES1350 205 (TTTA) 9 AGCGTGGTTCACACTGC GACCACAGGAGTTCAGCATCAGC 55 AU01028B2A03.f1 AUBES1351 200 (ATT) 8 GCATTCCACATGCTCACCTC GACACTGATCTCGTCCACGGTG 57 P AU01031B2B07.f1 AUBES1352 335 (TG) 15 AAGCTGAACGTCGTTCCAC GACGCAGTCCAGATTGTGTGACG 50 AU01021B2H09.f1 AUBES1353 235 (TG) 9 TCACCAGCTTGCTCTGAGAC GACGTCCTCCTGAAGTCCAGACG 50 P AU01032A2H07.r1 AUBES1354 385 (CA) 30 ACAACGCTCAGTTGCTGGAC GACACACTGAAGCGGAACGATGG 55 P AU01025B1H02.f1 AUBES1355 175 (TG) 22 CACCGCAGTCGGAATCCTGG GACCACAGACACGGAGACGCCTG 55 AU01032B1F09.f1 AUBES1356 142 (CA) 21 GCGCAAGTTTAAGGATGTGTGC GACCCCCATTCACCTCGACATGG 50 AU01020B1H06.r1 AUBES1357 330 (GA) 19 GTATTCTTCACTTAGGGCAAGGTC GACGTATGCTGCTGATGCTCAGG 50 AU01010B2B03.f1 AUBES1358 478 (TG) 11 TGACTGTGTTTGCCCAGGTG GACCAGTGGAATGTCCTCACAAGG 55 AU01011B2C12.f1 AUBES1359 420 (AC) 27 ACACTTCTCAGGCTCTCCAG GACGATTAGGGCACTGAAGTCACG 55 AU01011A2C01.r1 AUBES1360 340 (AC) 19 CCCCTGAACGGAGCAG GACACTCCATTGGTTTGAGGCAC 55 AU01024A2D06.f1 AUBES1361 284 (TTA) 8 GTTTGAGTCGGCAGCACTAC GACTATTCTGGCTGGAGGCTACG 55 AU01034A1A01.f1 AUBES1362 323 (CA) 8 TCTGCACCTTCACGCAG GACCACTGGGCACAGAGCAC 55 AU01034B1B02.r1 AUBES1363 304 (TG) 24 TGGACGGAATGGTCTGGAG GACTTTTCTCCTGCCGGTGG 55 AU01034B1F10.r1 AUBES1364 190 (GA) 11 GTCAACGCCGAGGTCAC GACGGAAGTCTAAGGCTGTGTCAC 50 AU01032A2F10.r1 AUBES1365 322 (TG) 10 GCTTGTTCCATCAATAGCCAGC GACTCTCAAAATGGTGCTGGAAGTG 50 AU01018A2C02.r1 AUBES1366 380 (AC) 8 TCAAAGCAGCAGCCTTCTC GACACTCTCCAGGGGTGACG 55 AU01021B2F01.r1 AUBES1367 172 (TA) 25 AAACTGCTACTGCACTGCTC GACCCTCGTCAATGCTGTAGTCC 55 AU01030B2G12.r1 AUBES1368 114 (GA) 9 TGTTGAATGAGGCTGTCGTG GACGAAATCCAGTCAGGGTCGTC 55 AU01026B2E02.r1 AUBES1369 188 (GA) 8 CAGAGAAATCTGTCTTTGTGCTCC GACTGAACGCACACGCTGAC 55 AU01021A1C09.r1 AUBES1370 83 (TAAT) 5 ACAGCAGCCTCCACAAC GACGGCCAGGTCCGTGTAC 55 AU01029A1H05.f1 AUBES1371 367 (TG) 15 CACAGAGGTTCTGCCATTACG GACCATCTCTGCCTCCAAAGCTG 50 AU01022B1H04.f1 AUBES1372 283 (CA) 9 TGCACACATACATGCTGCTG GACCCTGTGGTTTGGTGGGTG 55 AU01021A1H07.f1 AUBES1373 303 (CA) 16 TGAGCGTGTGCCAGTG GACCATCGGGCAGGTCCTG 55 AU01022A1F10.r1 AUBES1374 51 (CA) 26 CTCCCGCACAACAGACG 54 GACGAGCTGAGCTGAGCTGC AU01019B1B07.r1 AUBES1375 116 (CA) 16 CATTCCCTGCGAGTCTGC GACGTCCACCCGCACAGAC 55 AU01033B1A08.r1 AUBES1376 121 (TG) 16 TTCTTCAACTGTGTGGATGAGC GACCAGTCCTGAACTCACACTGG 55 AU01025B2F07.r1 AUBES1377 142 (CA) 19 CACATACACACCTCCATGAGC GACCAGTTACAAGGGGTTTCCCAG 55 AU01034A1G06.r1 AUBES1378 205 (GGA) 7 CAGTCCTGACTTGCCAGG GACCCAGTGGACAAAGCCTGC 55 AU01020B2G10.f1 AUBES1379 254 (TAA) 12 GGAAACAGACTCCACACTGAG GACGTCTGGTGAACGGGTTTGAG 50 AU01023A2D07.f1 AUBES1380 80 (CA) 30 CTCAGAGACCAGCAACACTG GACCCTTAATCCACACTGCATGGAG 55 AU01023B1F03.r1 AUBES1381 205 (CA) 13 ACCTGAATTAGGAGGCTACCTG GACAGTTCAGTTCCGCAAGCTG 55 AU01018A1B08.r1 AUBES1389 423 (AACA) 10 TCATGGCTCCAAGGTTGC GACTGCCATCTTGCCATTCCTG 55 AU01018A1E03.r1 AUBES1390 492 (TG) 19 CAAGTGCTGCACGGAGC GACGGCTCGGTTTGGCTCTG 55 AU01018A1E05.r1 AUBES1391 495 (TG) 17 TCATGCCAGTGCATCACAG GACCAGGTTCTCCGGTTTCCTC 55 AU01018A1E08.r1 AUBES1392 187 (GA) 19 AGAGAAACCCTAAACTCACAGTCC GACACCGTGTTTTCTGAGCCAG 55 AU01018A1G08.r1 AUBES1393 400 (TGTT) 6 GAATGCCTGACTCTGGGAG GACTGGTGCTCGGGAGTCTC 55 AU01018A1C04.f1 AUBES1394 310 (TTTC) 8 TGGGTTGTAGAGGTATCCTGC GACGGGCATAATGCTTTTGCAGC 55 AU01018A1D06.f1 AUBES1395 251 (AC) 20 CTTCCTAATCTCTTGTGGACAGC GACGTTCTGGGGTCGCCATTAC 55 AU01018A1D08.f1 AUBES1396 139 (AACA) 5 TGTAACCCTAGCCAGCTACAG GACGCTGCTACTCTCGCATGAC 55 P AU01018A2B05.r1 AUBES1397 155 (TG) 14 TCCCTCTGACTACACCAGC GACGACATATTGGGCACCCCTG 55 AU01018A2B06.r1 AUBES1398 452 (AC) 14 TTGTGAAGCTGGTGGACG GACACAGAAAGCGTTCAGCAGC 55 AU01018A2C01.r1 AUBES1399 160 (CA) 8 TGTGTCCAGGGTTGTGC GACAACCGCACTTCACCGAG 55 AU01018A2C01.f1 AUBES1401 470 (TTAT) 25 GCTAGTTCTCCAGGATGCAAC GACGTTAGCGACGACAGTGTTGG 55 AU01020B1A04.f1 AUBES1402 73 (AT) 35 ACTCTGGCCTAGCACCATGT GACACCTTTCAGTGAGGGCGTTT 60 AU01021A1B12.f1 AUBES1403 241 (GA) 11 CCCGAGGGTAAAAATATGGA GACCAGCCTGTATATTCCATGCAGA 55 AU01021A1C04.f1 AUBES1404 209 (TC) 20 TTTGTTAAATGGCCGCTAGG GACTGCTCAACTCCTGGTACTGC 55 AU01021A2F07.f1 AUBES1405 290 (TC) 20 CAAAACATGCAGCCTGAGAG GACTCACACAGCTGCTGATCAAA 60 P AU01021B1D01.f1 AUBES1406 178 (CTAA) 14 TTCGTTAGTTAGTTCGTTCGTTC GACCCCCCAAGAACTTGAGGTAA 55 P AU01022A2B12.f1 AUBES1407 160 (AC) 23 TCAGGTTTGCACATCACTCTG GACCAACCCCTCATTCACGAGAT 55 AU01022B2D01.r1 AUBES1408 30 (GT) 12 CCCATGTGGGTTATTTCCAC GACGATGTGTTGTGAACGATGTCA 60 NP AU01023B1B07.r1 AUBES1409 348 (AG) 17 TTTTTACACGCCTTCCCAAG GACGAAGTGCTTTGGATGGAACC 55 AU01023B2G08.f1 AUBES1410 468 (AC) 19 GGGAATCGTTACGTGCTGTT GACAGACCAGATGCATAGGTGAGC 60 P AU01024B1H05.r1 AUBES1411 314 (TG) 14 ACGCTGTTAGGGGGTTGAT GACGTGATGAGGAAAGGGACAGC 55 AU01024B2A09.r1 AUBES1412 108 (ATT) 11 TGCCACCAATAACAGACAACA GACGTCCTAAGGCCGGGAAATAG 60 AU01026A1C02.f1 AUBES1413 563 (ATGG) 8 CGTGGATACTGCTCTGCGTA GACCCCACGACCCTGTAGGATAA 55 AU01026A2C11.f1 AUBES1414 370 (ATGG) 7 GCGTCTCTTTGCTTTTCTCG GACCTGGGATAGGTTCCATGCTC 55 AU01026B1A11.f1 AUBES1415 319 (AT) 29 GAATGCCCTAAGTGGTCATGT GACTGGCCATACTTATACTCTTACTCAAG 60 NP AU01027A1B06.r1 AUBES1416 368 (GT) 15 GGTGCCATCACATGTCTCAC GACTCCTTCATGGTGGAAACAAA 60 P AU01030B1A06.r1 AUBES1417 319 (TAT) 10 TTGTGTTGGCACACAGATCA GACTCTCCGCTAAGAGCTCACTTG 55 AU01032A2G10.r1 AUBES1418 239 (TG) 12 TGTCAACATGTTAAGCACACTAGC GACTGATGGGGAAGCTGAGAGTT 53 AU01018B1A07.f1 AUBES1419 197 (ATG) 6 GCCACATTTCAATTTGGGCTC GACGTCAGTATGTAAGCTCCCAGC 55 AU01018B2B09.r1 AUBES1420 315 (TG) 17 ACGGTCCTACACACTCCAG GACGTGACGAGTGGCTGAAGC 55 AU01018B2C07.r1 AUBES1421 220 (TC) 14 GTGATGAGTCAATGCAACTCAGG GACACAGACGCATGACAGCTTC 55 AU01018B2E07.f1 AUBES1422 414 (CA) 11 GACTGCTGAATTTAAGTGAGGGAG GACCCATAACGCTTCCAGAGTGAC 55 AU01019A1E10.f1 AUBES1423 409 (TG) 12 GCAGCTCTGCACGCAC GACACTGTGCAAAACAGCCTCG 55 AU01019A1G12.f1 AUBES1424 173 (AC) 25 TGTCCGTAGATACTGGTGGAC GACGGAAGCTGTCACAAGGTTGC 55 P AU01019A2A03.r1 AUBES1425 496 (AC) 9 TCGCTGTGCAGCAAGTC GACAAACATGATACCACACTGTCTGC 55 AU01027B2C09.f1 AUBES1426 393 (AATA) 5 TTGGCTTCATAACAATTCCAAA GACCCTAACCAGCTTCCCACAAA 55 AU01029B1F07.f1 AUBES1427 316 (TAA) 8 CGCTCCTACTGTGGTGATTG GACTTGGTTTATCAGCGGGACAT 55 NP 55 AU01030A1F10.f1 AUBES1428 369 (GT) 13 TGATGGAGGAGTGAATGCAA GACGTCTTTCGCACCCTGTCTGT AU01032A1D01.f1 AUBES1429 459 (GT) 10 CATCACAGGCCACGACTG GACTCCAAAGACATGCGCTGTAG AU01032A2C10.r1 AUBES1430 332 (CA) 22 TCAAGCACTGGTAAAGAACTGG GACGGTTCTGTGTGCCTGTCAGA 57 P AU01032B1F12.r1 AUBES1431 394 (CT) 10 AAGGCCTCTTACCTAAACAGCA GACTTGAAACTGGACAGCACTGG 55 P AU01032B2A11.r1 AUBES1432 476 (GA) 28 CATTCATTCATTCAGTCGTTCG GACTCACCTAAAAGAATCGGCTCA 55 AU01019A2A11.f1 AUBES1433 445 (GTT) 6 TGAAGGTGCATTTGCATTGT GACCAGCAGGGCACATTTTCAG 55 P AU01020A1F12.r1 AUBES1434 96 (AC) 9 GAACATCACATCAAGTGGAGGA GACCTGGGTCTCCTTCAGCATCT 55 P AU01020A2C05.f1 AUBES1435 450 (CA) 17 GCTGATGCCATGCTAGTGTT GACGTGACATGGCTCTGCTAGGC 55 P AU01020B1C12.f1 AUBES1436 145 (TTA) 14 CCCCTAGGGAACCTGAACAT GACGAACCACTGTCTTGCAATGTG 55 P AU01020B2B03.f1 AUBES1437 455 (TATT) 6 TTACGTTCCATGACAGTGACG GACATATGAAGAGGCCCGTGAGA 55 NP AU01020B2G10.f1 AUBES1438 255 (TAA) 12 TGGAAACAGACTCCACACTGA GACGGCTCAGACTCTCCTGTCAGA 55 NP AU01021A1E06.f1 AUBES1439 180 (AC) 20 GGCTCCCGAGGTTTAGAGAT GACGAAAGGGCCTCGTCTTGAG 55 NP AU01021A2G02.r1 AUBES1440 450 (AC) 14 TGCTGGACTCAACTCACAAA GACCCTTGCCAAGTGTGTGTAGAA 55 AU01021B2C07.f1 AUBES1441 340 (TA) 13 TGGTACAGAGAGAAGGGGACA GACCATCCAGACCTCTGAGGACATA 55 AU01022B1G08.f1 AUBES1442 187 (TA) 29 TGGCTATAGTCAGGGGTAAGAGA GACTCGAGTAAATGATGTTAGCTGAGG 55 AU01022B2E01.f1 AUBES1443 217 (GT) 22 CTGTCTGAGCTGGAATTGGA GACGACCAGAATGCCTGCAGATTA 53 AU01023A1E08.r1 AUBES1444 415 (TG) 14 CCTACGCACTTGACACCTTG GACACATGGGCACGTGTGATGTA 53 P AU01025A1C11.r1 AUBES1445 241 (AC) 23 AAGAGGCAACACGGAGTGAT GACCAGGACCAAGCTTCACTGAG 55 P AU01025A1E02.r1 AUBES1446 96 (TG) 8 GAGAAACCAGGCTTCAGCTC GACCGTCCTCAGACGGTTTCAGA 57 AU01025A2D08.r1 AUBES1447 359 (AAC) 8 ATACCAGCGTTTCCCAAATG GACTGTGGGATCTTGATTGTTGG 55 AU01027B2C11.f1 AUBES1448 181 (GT) 9 GGTCAGTGTCCCCTCAGAGT GACATGCAAGCAAATCGAAATGG 55 P AU01028B2E03.r1 AUBES1449 318 (GT) 12 TGATCTGTGATCTTCTGCACTG GACCAGCTATAGTGCGCGTGTGT 55 AU01032B2C11.r1 AUBES1450 139 (TA) 12 CGTTCATTTTGCTGAACGAG GACGGAAGCAGGAGCATCAGAAA 55 AU01032B2H09.f1 AUBES1451 446 (TA) 33 GGAACCCCAGGCTACATCTT GACCACACCCTTCTGTCACTTCG 55 AU01021B2A12.r1 AUBES1452 111 (CA) 8 GCATACTGTATTTGGGCATGG GACTGCATGTAATGGATGCGTCT 55 AU01019B2G05.r1 AUBES1531 108 (AT) 14 ATTACGCACTCTCGGACTCG GACGGAGGAATGCAACAGGTACAA 57 P AU01020B1G11.f1 AUBES1532 544 (TA) 28 CTTCCCCTTTTTCCAATCCT GACCCGGACTGATAACATCAAGACA 57 AU01023B2F11.f1 AUBES1533 478 (AAT) 23 AAGCCTTCCTGTCTGTCAAAG GACCAGCAAACAATCTGATGTGGA 57 AU01025B1F11.r1 AUBES1534 404 (AT) 8 CCCGCAGGAATTCTATAAAGG GACGCAGGAATGTCCAGAACACA 55 AU01028A2D06.f1 AUBES1535 240 (GT) 13 ATGCACACGCATACACACG GACCCTCAGTAACTGGCAATCACA 57 NP AU01029A2A02.f1 AUBES1536 314 (AC) 11 TGCTGGCTGTAATTTGAACA GACTCCATGGGGTGTACTTGTCC 55 AU01029B2A11.f1 AUBES1537 186 (TA) 8 CCTAGGAACCAACATCGTTGTAA GACGCGGTACTGTACTTCCATCCA 57 AU01030B1G03.r1 AUBES1538 193 (AT) 30 TGAGGGGTGTACTCACTTTTG GACTTGCATCGGTGCATCTCTAA 57 AU01030B2B01.f1 AUBES1539 162 (TG) 9 GAGGCTCACTCCTCCATCTG GACCTCAAGACACGGTGACCAAA 57 P AU01031A1D05.r1 AUBES1540 174 (CA) 18 CAGTAGGTGGAATGGCCAAA GACGGTACACCAGTCCATTGCAG 55 AU01031A2A11.r1 AUBES1541 86 (ATT) 18 GCTGTAAGCAGCCATGTTGA GACTGAGAAGCTGTTTTTAAGGTGCT 55 NP AU01032A1C10.f1 AUBES1542 457 (TA) 31 GCAACCAGGATCTTGTGTGAA GACATGGGTTTGGTTGCCAAGTA 55 AU01029B2H01.r1 AUBES1543 111 (TC) 8 ACACTGGTGGGTTGTGACCT GACAACGATGGAAGCAAGTCCAG 55 NP AU01019A2C08.r1 AUBES1544 95 (ATTT) 6 AATGCAAGTGGTACAGCCCTA GACTTGCCTATAGTTTACGATCACAAT 55 P AU01021B1E11.f1 AUBES1545 493 (AC) 15 CGTAGCTGCCTATCTGCCTTT GACTTGATTAGGGCTGAGCAATG 55 AU01024B2C11.f1 AUBES1546 79 (GT) 18 ACAGGCCATCAAATTCCTCA GACCAAGGGGAGTGAAATGGTGT 55 AU01025B1A12.r1 AUBES1547 168 (TATT) 5 TGGCCCAGTGAAATCTGTTT GACCTGGAAGCAATGTGTTGTGC 55 P AU01026A1A11.f1 AUBES1548 172 (AG) 10 GAACGGCATGCTCTATGACA GACTGAAGATGGACTGCTTTGCTT 55 AU01028B1B09.r1 AUBES1549 283 (CA) 23 CCTCCCACCAGATCAGTGAA GACAGGTGCAGCACTCCTAAACG 55 AU01028B1F04.r1 AUBES1550 460 (TC) 8 AAGGAGCTGAGATCTGCTTGG GACGCGTGGCAATAATATAGATGTCG 55 AU01029A1A06.f1 AUBES1551 231 (AATA) 7 AGGGTTGGAAGCAGAGTTGA 56 GACGCTCAAAGGACAGCAGAACC AU01027A1F04.r1 AUBES1552 112 (ATTT) 6 GAAAGCGCAACACATTGAGA GACGGAGTGGAACGCTGTGTTT 55 AU01025B2A06.r1 AUBES1553 471 (CTG) 6 ACCAGCACGTCTTATCTCTGC GACATCCTGCCGATGCAATTTTA 55 AU01020A1B01.r1 AUBES1554 342 (ATT) 13 TGTGGGCACAAATGTGTACTT GACCCATGTGCTGAGTATGGCATT 55 AU01023A1C07.f1 AUBES1555 237 (AT) 29 GGGGACAGCAAATTTACACTG GACGGTGGATTCATTGGGTTTGA 55 AU01026A1H11.r1 AUBES1556 439 (AT) 34 TGCACATATTCACTGTCCATAGC GACTGCAGTTTAGCGTGTGCCTA 55 AU01026B1F04.r1 AUBES1557 117 (TG) 10 CGGTTATCGGTGTCGACTG GACTGTTGACACGTCCCATTCTT 55 AU01028B2A05.r1 AUBES1558 227 (AATC)5 CCACATGAGTGGGAGTGATTT GACTCTGCAACCTCCAGCCTACT 55 AU01032B2A03.f1 AUBES1559 497 (GT) 16 GGTGGCACTTGCAAAACAT GACCACAGACTCGTGGCTTTTTCT 55 AU01032B2D09.f1 AUBES1560 320 (GTT) 7 AGCAGGTGAGAGTGCTTTGA GACTTGTACAGTATTGTCATGGGTCTG 55 AU01019A1B12.r1 AUBES1561 294 (ATTT) 9 CATTCATTCTGGTACAATGCAG GACCAAAGGACATTCATGTGCAG 55 P AU01019A1C10.f1 AUBES1562 189 (GT) 10 ATCACACGCCATCCATCAT GACTTCTTTGCACTCATTCGTGTG 60 AU01019B1G09.r1 AUBES1563 418 (CA) 16 GGTCAGATTAACCGCACTGA GACCGTGTTTGGAAGGCTGTTGT 57 P AU01021A1A06.r1 AUBES1564 286 (ATTT) 9 TGCACGTTGTTCCTTTCATT GACGCCTCGTAGAAACCAAGGTG 55 P AU01021B1B10.f1 AUBES1565 227 (TG) 10 CAGTTTCACACAAACCCTATCG GACGCCCTGACCTCTGATTCGTA 57 P AU01021B2D07.f1 AUBES1566 230 (AC) 9 AGTCTGACCCCAGCAACACT GACTGCCTAATCAGTGTCGCAAT 55 AU01022A1F04.r1 AUBES1567 443 (TG) 15 GGCATTTCATTTCAGGACTCA GACGAGGAATGTCGTGATCTTTGC 55 AU01022B1G07.f1 AUBES1568 119 (TA) 15 ACCCACGCCTTGACCTTT GACCGTCGACATGGATCAGTCTT 55 AU01023A1C10.r1 AUBES1569 441 (CA) 19 CCTCTTGAAAATGAGCAAACG GACTGCATGATTGCCTCATACG 55 AU01023A1E06.r1 AUBES1570 84 (TCAA) 7 GCAGACTCAAAACACAGCAAA GACATGGCACACAAAAGCATGAC 55 P AU01024B1C02.f1 AUBES1571 147 (GT) 11 GAGGGGTTCAGAGCATGTTT GACCACACACACATTCCTTTCCAA 60 P AU01025B2F04.r1 AUBES1572 327 (GT) 15 CTCAGCACCATATGCCACAC GACGGCTGTTTGCCTCTTAGCAG 57 P AU01026A1F08.f1 AUBES1573 390 (CA) 25 AAACAGTTCAGACTTCAGTGCTC GACCAGAATGCACGCTGAAAGAG 55 AU01027B1G10.f1 AUBES1574 178 (CA) 10 AAATGCCGAGTCAGGAGTGT GACGGTTTCACTGTGCGTTGATG 55 AU01028A1C07.f1 AUBES1575 484 (AT) 9 TGCATGGGGAATCTTTTCAT GACGCCTTCACACGATGTCAAAA 55 AU01028A2D07.r1 AUBES1576 186 (AC) 11 CTGAAGCTCCATCCCAACAG GACAACCCGAGCTGGAGTGATTA 57 AU01028B1C08.f1 AUBES1577 487 (TAAA) 5 GGAGTCGCAGTCCTAAGTAGC GACGCACAGAAGGGCATTCTACA 55 P AU01029A1A11.f1 AUBES1578 331 (GA) 28 CACACTCCTACAGCCCTGCT GACGAAACATCAGCACCAGCACT 55 AU01029B1C07.r1 AUBES1579 111 (TTA) 18 CAACTGGTGACCTGCAGAAA GACGGCAGTAACACCATCAGAGGA 55 AU01030A1H03.r1 AUBES1580 522 (ATCT) 6 AGTACCACGGCTGTTTGAGC GACTCAGTACACACAGGCTCTATCCA 55 AU01031A1B05.f1 AUBES1581 232 (TATT) 9 TGCCTTAGTGTTGCTTCACAG GACGATGGTGTCATTAGACAGTGCAA 55 AU01032A2E08.r1 AUBES1582 227 (AT) 29 TTTTCGGCCCAATTACAGAG GACGCGAGATGCCTTGCTATTTT 55 AU01032B1H12.r1 AUBES1583 371 (TG) 20 TTTCCCACTCACCCATTCAC GACCTCCCAATGGCAGGCTAAC 55 AU01032B2D06.f1 AUBES1584 201 (GT) 13 TCCTTGCAATAACAGGGATGT GACAAACGCATCGCTTCCATTTA 55 AU01018B1C05.f1 AUBES1585 272 (AC) 18 TCCTGAAAACATGTCAGTTGG GACCTGGAATACACCCTGCATGA 55 AU01019B1G08.r1 AUBES1586 399 (ATA) 12 CAAAACGCCAATGACATGAT GACTCCTCTCATCATCACCACAGT 55 AU01020A2D03.f1 AUBES1587 272 (AT) 31 TGTTACCACCCCGGAAATAA GACCAGGAACTTGTATTGCGGAAG 55 AU01021A1E04.r1 AUBES1588 411 (CA) 23 TTTTGGTCCAGAACATGGAG GACCCTGTCATTCCCTGCTTAGTG 55 AU01022A1G01.f1 AUBES1589 73 (GT) 15 TCGGCAAAATCCACAAAAGT GACTTTGAAATCGCATTGTAGGC 60 P AU01023B2G12.f1 AUBES1590 232 (ATA) 12 CGCTTTCATTCGAAGCAACT GACTGTCAACTTGGACCTAATGTGC 55 P AU01029A1C01.r1 AUBES1591 550 (ATT) 10 GCCAAAATGCCGGACTATCT GACTGCAGTTGAGCTCTCGGTAA 55 P AU01029B1A08.r1 AUBES1592 314 (CA) 12 GGTGCGTTCAAGAGAAAGGA GACCCCTTTAAAGAGGGCTTTTCC 55 P AU01029B2E02.f1 AUBES1593 422 (GT) 10 CATTGCCTCGTCCAGAGAATA GACAATGGCATTTGGCTGAAGAG 55 P AU01020B1E04.f1 AUBES1594 134 (GT) 24 CCCTCAGTGTTGTGAACCTGT GACAAATTAGGCCACGTGTAGGG 55 AU01021A1F09.f1 AUBES1595 283 (AAAT) 5 CATGGGCATGATCACAGACT GACGGGTGCAATTTTTCACATGG 55 P AU01021B1B01.f1 AUBES1596 327 (GT) 12 GTCCTGTGACCATGTGACCA GACGAAAGACCACCAGTGTGCTG 55 57 AU01023A1A10.r1 AUBES1597 352 (GTA) 12 AAAGCGATTCCCCATCATC GACTGGAGCAAACAACGTTTGAG 55 P AU01023A1E07.f1 AUBES1598 107 (TAT) 8 TTGCATCAAACCTGAAATGC GACTGAAGTGACCTTGGGTGTCA 55 AU01023B2B03.f1 AUBES1599 213 (GT) 18 CGCTCATGCTCATCACCTC GACTGAGGAATAATGCCACCACA 55 AU01024A1A03.r1 AUBES1600 367 (GA) 27 CCGGGGCTTCTATAGCACAT GACCTCCCTTCACAAGCTGTTCA 55 P AU01028B2D02.f1 AUBES1601 398 (TG) 20 CAGCACATCCTTCTGAGTGC GACTCCCTGCATTCCTCTCAGTT 55 AU01029A2B08.f1 AUBES1602 482 (CA) 11 CTGCCATCTCCAAATTGTCC GACAGGAGCGTGGAGCCTATACC 55 NP AU01029A2F05.f1 AUBES1603 236 (AC) 13 CTGCAGCAGAACAGCACATT GACAGCAGCCCGCATTCTATGTA 55 AU01029B2G10.f1 AUBES1604 149 (TC) 20 TTTTTGGCGGACGAACAC GACTCCTCAGCCCACACTTCCTA 55 AU01031A2E08.f1 AUBES1605 85 (AG) 11 AGCCAGATCCGATCACTCAG GACGGGTTAGGCGTTAGGGGTTA 60 AU01031A2G03.f1 AUBES1606 335 (AC) 16 ACAGCCAGATGATTTCCAGTT GACGGTTAAACAGCTAGGTGCACTG 55 P AU01032B1F02.f1 AUBES1607 352 (TA) 32 CCAGTCCGACATAGTGAGGA GACCCACCATGTGCCCAGTCTAT 60 P AU01021A2E04.r1 AUBES1608 433 (TG) 9 CCACTTCACACTGCCGTCTA GACCCCTACTTGTGCCTGAGAGTG 60 P AU01021A2E10.f1 AUBES1609 363 (ATG) 9 CCGGCTCTAATGATGCAGTT GACAATTGGGATGAATGGATGGA 60 P AU01021B1A10.f1 AUBES1610 466 (GT) 12 CGCTCACTACATAGGGCATGA GACACTCGCTGAAGAAGGCATTT 60 P AU01023B1C07.f1 AUBES1611 611 (AC) 12 TAGCCCGTACGTGTTTATGC GACGTGATCGAGGCTATGCCATT 60 AU01024A2B01.r1 AUBES1612 148 (CA) 17 GTCTCTTTTCGGTCCAGACG GACTACCAGCCTTCCAAGCATTC 60 P AU01024B1A09.f1 AUBES1613 324 (TATT) 8 TCACGTGACCACACGTTACA GACTGTCCTGAATTGCGTAGTCG 55 P AU01024B1G07.f1 AUBES1614 91 (CA) 9 AAGGCTGGACAAGCAATGTT GACCCCTAACTGCTAAGCCATCA 60 NP AU01024B1H04.f1 AUBES1615 77 (GT) 20 TCCCTTAAAGCCCTCAATCA GACTGATGCCTGGCTGAGAGATA 60 P AU01026A1G06.r1 AUBES1616 257 (CT) 8 TGGATCAAAGTCCCCAATTC GACATGGATCTGGCACAATGGAT 60 P AU01026A1G03.f1 AUBES1617 122 (AG) 16 AAACTGCGTCGAGTTCCACT GACGCGCTCCTCAGTCTCTCATT 60 P AU01018B2A04.f1 AUBES1618 138 (AC) 27 GAGTTCGGAGAAAGCACACC GACGCTTCATCCACCTACACATGC 60 P AU01020A1A11.f1 AUBES1619 525 (ATAA) 5 GCGAGATACTGCCGTTTGAT GACCACCGGAGACAATGTACTGG 55 P AU01020B1H12.f1 AUBES1620 225 (GAA) 7 GTGGAATAATCACGGCTTCC GACCACGTGTTTTAGCCTGTCCA 55 P AU01021A1G03.f1 AUBES1621 299 (TG) 8 GGGAATACTTTGTGGGTAGTGC GACCCCTGACCAGGATACAGTGG 55 P AU01021A2F06.f1 AUBES1622 209 (ATTT) 6 GCAGGCACTCCACAACATTA GACCCATGCAGTAAGGGGTTCAT 55 NP AU01022B2E04.f1 AUBES1623 199 (GT) 9 CCTGACCTGCACACTCATTC GACAGGGAGTGCAAGTTGTGGAA 55 NP AU01023A2H12.r1 AUBES1624 432 (CA) 11 CCTCTTTTAGGTCGGCTGAA GACAGCACTAAGCACAGGTGCAA 55 P AU01026A2C01.r1 AUBES1625 263 (TGG) 7 TCTGACTGCTCGGGTTTACA GACCCACCATGTCCGTGACAATA 55 P AU01027A2E03.r1 AUBES1626 175 (AT) 9 TGGTACACGATCATCTTCCTGA GACAGTGATTGCACATTCACAAGG 55 P AU01029B1F01.r1 AUBES1627 563 (ATGG) 7 GCCAAACAGCGACAACTCTT GACTGGGATAGGCTCCAGGTTC 55 NP AU01030A1C12.r1 AUBES1628 94 (TATT) 9 GAAGTATGCATGGGGATTGG GACACCACTCACCTGTGCCTGAA 55 AU01031A2A05.r1 AUBES1629 425 (AG) 9 CAGAGCACTTGCATCAGGAG GACCACGCCTACAAAACTCCGTA 55 P AU01032A1H08.f1 AUBES1630 405 (CT) 11 TTCCCTGTCTGAGCGAGTCT GACGAGCGCGAAGGTAAGAGTTG 55 P AU01019B2H06.r1 AUBES1631 252 (AG) 22 CTGTAAGCTCACTGCCACCA GACCAGTGTGAGGTGAAAGCACTG 55 AU01020A1A03.f1 AUBES1632 273 (TG) 15 AAAGCCGGTACCTCATTCCT GACTCTGCACAGCATCACTCCAT 55 AU01020A2E12.r1 AUBES1633 221 (CA) 11 CACAGTGCTTTGTTGTGACG GACGTACCCCAGGTGTGTTTGCT 55 AU01021B1C06.r1 AUBES1634 230 (CA) 27 CCCTGGCGTTTTCAGTAGAA GACCGAGGACCTGGATCAGACTC 55 AU01023A1B02.f1 AUBES1635 74 (AC) 16 CAGTGGAATGTCCTCACAAGG GACACTGTGTTTGCCCAGGTGTC 55 AU01024A1A04.r1 AUBES1636 148 (AT) 17 GGTGCAAGGAAAATGACAGG GACGTCCTTCAAGCTGCCAGTGT 55 AU01019A2F09.f1 AUBES1721 329 (CA) 22 AGTTGGAGCCAGGTAAGTGC GACACCATCGCACCTAGCAAAAC 55 AU01020A1A08.f1 AUBES1722 203 (ATT) 12 CGACATTGAGGTTTGGAGGT GACGAGGTGAGGAGTGGCCATAA 55 P AU01020A1C03.f1 AUBES1723 487 (TTGT) 6 CATGAGCCTGACACTGGAGA GACCGGACGCTCCACATAATCTA 60 P AU01021A1A05.f1 AUBES1724 225 (TCA) 11 GCAGTGTGAAGCTATGTCATGTT GACGTGACTGGAAGCATGGGAAT 55 P AU01022A2H05.r1 AUBES1725 233 (TTAT) 8 TGTCATGTCAGTTGGAAGCA GACCCATGTATCAGGTTTGCACATT 50 P AU01023A1F08.f1 AUBES1726 216 (CA) 19 CAGTAGGTGGAATGGCCAAA 60 P 58 GACGGTACACCAGTCCATTGCAG AU01023A2C12.r1 AUBES1727 110 (GT) 12 TCGATCCGTGGCCTAAATAC GACCAAAACAGTCCTGGCTGACA 60 P AU01023B1C08.f1 AUBES1728 401 (CT) 20 GCATGAATGAGCTCACAAGC GACTTGGCCTCTAAATTTGTGCTC 50 P AU01024A1B03.f1 AUBES1729 194 (AT) 30 CAATGTAGCCTTCGGACAGC GACCTTGCTCCACAGCAAGAGC 55 AU01026A1G04.r1 AUBES1730 104 (TAT) 18 CAGCCAGGAGTCCAAACTGT GACTCAAACCAGTGTGCTTTCTCC 60 P AU01026B2G04.r1 AUBES1731 200 (TG) 16 CTACAGGTTCCCCATGGTTG GACGCGCTTAGCCACAATCATCT 55 P AU01026B2F08.f1 AUBES1732 199 (TG) 12 AGGGGACTCGAGGCAATC GACTGTTTAGAGAGGTGCCTTGC 55 AU01027A2E05.r1 AUBES1733 421 (AT) 11 AACTGACCGGAACTACCTGTG GACCGCATCCCAGCGTACAATTA 50 P AU01028A1C01.r1 AUBES1734 98 (CT) 16 ATCATGCCTTTCGACGTCTC GACGTTCACCGATCTCACTGCTG 55 AU01030B2B08.r1 AUBES1735 386 (TC) 13 AACTGAGCCAAGCAAACTGC GACCCTTTAAGGCCATCTGTTCC 60 AU01030B2C05.r1 AUBES1736 259 (AG) 9 GGTGAACAATGGCTGGAGTT GACCCCACCCTATGTCCTTAGCA 50 P AU01031A1F10.f1 AUBES1737 251 (TTA) 6 AAAGCAAGCAGTCATCACGA GACTGCTCAGCACTGAAATCACA 60 P AU01031B2C07.r1 AUBES1738 345 (TG) 24 GCAAGATGGGATTCCAGTGTA GACACTTCCCAAAAACCTGCTGA 60 P AU01032B2A06.f1 AUBES1739 203 (GT) 13 TCCTTGCAATAACAGGGATGT GACAAACGCATCGCTTCCATTTA 55 AU01032B2G01.f1 AUBES1740 126 (TTA) 16 CAGTGTCAGCATCCAAGAGG GACAACCCTGAACTTAAAACCCTGA 55 AU01019B1D12.r1 AUBES1741 483 (TG) 7 GGCTACACACACTTCCCATATC GACGAGGGGCAGGTTAATACGAA 60 P AU01020A1A02.f1 AUBES1742 173 (ATA) 8 TGCGTAGGTCAGATCCCTCT GACGCCCAGGGGTCCAATTAC 60 P AU01020A1B07.f1 AUBES1743 427 (GT) 9 CGCTGAAGAGCAGAGAGGTT GACTGGCTAACGACATGTGACCT 60 NP AU01020A1F05.f1 AUBES1744 276 (TAGA) 7 CATCCTTCCTTTCCTTCACG GACTTTGATGCTGCTTTCACCTG 60 P AU01021B1F04.f1 AUBES1745 245 (AC) 5 AATTGGGCTGCTCTCAGTGT GACCACAGGACAAAGCTCGTTCA 60 NP AU01021B2A03.r1 AUBES1746 135 (TG) 19 CAGGCATGCGAGAGTGTATC GACAGCATGACGCACAGTTCTTG 60 AU01022A1E09.r1 AUBES1747 97 (TCC) 8 GCAACATGACAACTCGGCTA GACTTCTGCTCAGAACCCTTTGC 60 P AU01028A2F04.r1 AUBES1748 326 (GTTT) 7 CACCTGCAACTGCACTATGA GACTGACTGTGAGTAGTTTCCCTGCT 60 P AU01030A1E12.f1 AUBES1749 437 (TC) 15 CTGTCTCGAAGCTGTGCTTG GACACCTTGGAGAGGGAACGTCT 60 P AU01032A1F10.r1 AUBES1750 326 (CA) 13 CTGCAGCCAGCTCTTTCTTT GACTCACCATATGACGGAATGGA 60 AU01032A2B02.f1 AUBES1751 485 (CA) 29 AAATGGAGCGTTATGGGATG GACCTGCATGTACTGCCCATTTC 57 AU01018A1C09.r1 AUBES1752 188 (TA) 37 CTCTTCCCCACCTCCAAGTA GACGGGAGTACAGTAAACCACTTGC 57 AU01023B1D06.r1 AUBES1753 380 (AG) 27 CATTGGGAAAAGCCCTCTAA GACCGTTGAATCATTTGGATTGC 57 AU01024A1G05.f1 AUBES1754 154 (TAA) 12 TGTAAAGTGCCTTGAGAAGCTG GACAGAGAGGCCTTTTCACAGCA 60 P AU01024B2B05.r1 AUBES1755 211 (AC) 20 TCTCTCAGAGGGCATGTCTG GACCGACAGCTTTGTGTGGAAAA 60 NP AU01024B2D02.r1 AUBES1756 134 (AT) 36 CCGGCATTTCACGGATATAG GACAGCAACGTAACAGCCAGTCA 55 AU01026A2A05.f1 AUBES1757 49 (CA) 10 CGAGACACTGAACCCCAAGT GACCGCTTTACGCTGGTTCTCAT 60 AU01027B2A02.f1 AUBES1758 130 (AC) 10 AGCTGGGACAAAAGTCTTGG GACGCCAACCTTCCTTTGCTCTA 60 AU01027B2B05.f1 AUBES1759 150 (CT) 13 TGGAAGATGGTACGGGAGAC GACGCCCTCTGGCAATAACAAGT 60 P AU01028B1A03.r1 AUBES1760 244 (GT) 8 GCGGTTTGCCTTACACAATC GACGACTTCGCGTTCCGTAGTTC 60 NP AU01028B1D05.r1 AUBES1761 201 (TAT) 10 GCTGCATGTTCCTGAGGAGT GACGAAACACTGGGGATGAGGTG 60 P AU01030B2C11.f1 AUBES1762 359 (TTAT) 7 CCCAAACTGACGGAGTGAAT GACCGCTTTCCGTCACCTAAATG 60 P AU01032A2G02.f1 AUBES1763 487 (AC) 40 TCATGGCATGCATACTAACACA GACCTGCATGTATGTGGCTCCAG 60 P AU01032B1H08.f1 AUBES1764 431 (AAT) 16 AGCACGGAATGACTGCTTTT GACCAATGTCGAGGCCAATCTG 57 P AU01032B2H02.f1 AUBES1765 427 (ATTC) 9 GGGACCAGGGTAAAGCAGTT GACGCCCTTGTTTGTTCATGTCC 57 P AU01019B1B01.f1 AUBES1859 319 (AG) 11 TCTGATGCCAGAACTTGTGC GACTCACACCATGTGCTTCAACA 55 P AU01019B1D10.f1 AUBES1860 119 (TCCA) 5 CTGGGATATGCTCCAGGTTC GACGTTGCCAGTGGAACATCTCA 55 P AU01020B1E09.f1 AUBES1861 357 (AT) 11 GCATAGGAGTGGAGCTTCAAA GACGCCATTTATATTGTGCCTGTTG 55 AU01021B1G12.f1 AUBES1862 294 (TA) 32 TCAGCCTAAAGCTTTCAATTCC GACGGCTCCACGTTTTATGTCGT 55 AU01021B2H11.f1 AUBES1863 164 (CA) 8 GGGTGCAGTCTATGCAGGTC GACAGTTGTTGTCAGGGGCTTCA 55 P AU01022B1A02.r1 AUBES1864 173 (GAA) 13 TTCCCCGTATGAGTGTAGGC GACCGGTCCACAAGTGGTAAAGAA 55 59 AU01022B2C01.f1 AUBES1865 451 (TAT) 14 CGTGTGGAGCAATTTGAGTG GACTGAAACTGGATCAGAGGCTTT 55 P AU01023A1G06.r1 AUBES1866 156 (TG) 11 TGTCTCAATCCTTTGCATCA GACGCTGCATTCTAGTGCAGAACC 55 NP AU01023B1E10.r1 AUBES1867 221 (AAT) 17 TCTGGCAACTTTTACAGTGCAT GACTGTTCACACACATTGCTGACA 55 P AU01024B1A09.r1 AUBES1868 413 (TTA) 13 TCCTCTCCTGAGATCCTTAACA GACGTTCTCCAGGCCCAGTCAT 55 P AU01025A1E08.f1 AUBES1869 394 (TAT) 11 GAGCCGGGTTACACCTAACA GACTTGCTACAGCTGAGGCATGT 55 P AU01025A2G01.r1 AUBES1870 188 (AAC) 6 AAGGGTCTTGCCCTCTGAAC GACGGTGAAGCCCAATGATGCTA 55 NP AU01025A2E02.f1 AUBES1871 409 (CA) 13 CATGGACGCATTGTAGTGTTG GACTTGGATAGCTCACGGTGTATG 55 P AU01025A2G04.f1 AUBES1872 245 (AC) 18 TCCTGAAAACATGTCAGTTGG GACCTGGAATACACCCTGCATGA 55 P AU01025B1H07.f1 AUBES1873 109 (CT) 19 TTGCCTGCTGAACATGATTG GACCTGCCCTCTGATACCTGCAT 55 P AU01026A1E04.r1 AUBES1874 265 (AC) 11 CCCACTGTTCAACAACATGC GACGGATGGGACGTCAATCCAT 55 P AU01026A1D01.f1 AUBES1875 249 (TA) 35 CACGTTTCTGTTTAACGAGCAC GACCGTGGTACAGGGACAAAGGT 55 NP AU01026A2A04.r1 AUBES1876 322 (AG) 10 GGTGCTGAAGTGCCAAGACT GACCCAGCTGAAGTGAGATGGTG 55 P AU01026A2E12.r1 AUBES1877 246 (TCCA) 7 TGAAGGCAGGATAAGCGGTA GACCGGCCGTTTATAGCTTCTGT 55 P AU01026A2G10.r1 AUBES1878 427 (AG) 16 GAGTCCCTGCTTGCACTCTT GACTGAGGATCAGGCAACATCAG 55 P AU01026B1F12.r1 AUBES1879 409 (TG) 33 GGTGCATTAACCGTTTCTCTG GACTGAAGGTTGACAGCATCAGG 55 P AU01027A1F03.r1 AUBES1880 201 (AC) 40 ATGGGAACCTTTGAAGCTGA GACTTCAGGGTGGTTGTAGAATGC 55 AU01027B2B03.f1 AUBES1881 328 (ATA) 11 ATCGGGCCTTGAGGTAGATT GACTGTCACTTAAATCACGGAGGTT 55 P AU01028A1F11.r1 AUBES1882 491 (TGAA) 8 TTGATGATGGTGCTGGAGAA GACAACAAAGCTTGGCCTTATGC 55 P AU01028A2F12.r1 AUBES1883 319 (TG) 8 CCTGCGAAGTTTTCCTGAGT GACACTCCGGAACCTTGATTTCC 55 NP AU01028B1H01.f1 AUBES1884 118 (ATCC) 7 CCCGTGACCCTGTACGATAA GACCCCAGAAAAGGGATCTTGGT 55 P AU01028B1H10.f1 AUBES1885 354 (TC) 20 CATCAGGCTTTGAGCAACTG GACATCCACCCCCTTGTCTGACT 55 P AU01029A1A12.r1 AUBES1886 255 (GT) 30 GTTGCACTTGATGCAAAGGA GACGGCTCTTGACCTGAATTGTG 55 P AU01029A2C07.r1 AUBES1887 242 (ATA) 10 CAGCACAATGCAGTTTTGAA GACCATGGAAGGAGTCTCCAGTG 55 P AU01029B1H08.r1 AUBES1888 294 (AG) 31 GTGACGGAGCCTGTCTCTCT GACCCTGTTCCCAGATCAGAAGC 55 P AU01030B1D06.f1 AUBES1889 201 (TA) 33 GCTTTTGCAGATACCCAGAAA GACTCCGTTAATAATCGGCTGAGA 55 AU01030B1E03.f1 AUBES1890 328 (AT) 27 GCCCCATCCTGATTTCTTCT GACTCACACTTGCCCAGTTGGTA 55 NP AU01030B2F12.f1 AUBES1891 330 (CA) 17 GCAGAGAGTCATGTAGGGTGTG GACCGCACGACTCGGACAGTAAT 55 P AU01031A1D03.r1 AUBES1892 275 (AT) 10 TGCCATCAAGCGTTAGCATA GACGCATCACCATTCGTGGTCTA 55 P AU01031B2D10.f1 AUBES1893 354 (TAA) 14 CACTGAAGACATTTGGGTTTGA GACGCACACCAGTGGTTTCTTTCT 55 NP AU01032A1B12.r1 AUBES1894 96 (AT) 8 TGACATGCAGTCTTGCTGAAG GACAGGTGACGTGGCAATTAAGC 55 NP AU01032B1C11.f1 AUBES1895 428 (GT) 9 CCTCACCTGGAAATCCCATA GACGCATGGCAGCTCTGCTACTA 55 NP AU01032B1H04.f1 AUBES1896 229 (AAT) 6 TGGTTTACTTGGGACCATCTT GACTTCATTCAGCTTTGCGTCAT 55 P AU01019A2D06.f1 AUBES1947 343 (CA) 14 TGGACTCTGCCTTTTGATCC GACCAGATCCTGATCCCTGATGG 60 P AU01019B1F09.f1 AUBES1948 372 (AC) 11 GGAACTAAGAGGCCCAAACC GACCCACTCCAACCATAACACACC 55 P AU01020B1E12.r1 AUBES1949 343 (CA) 14 CATGCTGCTGATACGACTCC GACAGACCTCCATAGGCCACGTC 55 P AU01020B1F04.f1 AUBES1950 306 (TTC) 13 CTGGCCACAGAGCAGAGAG GACCTACACCATGAAGGGCCAGT 55 P AU01020B2D10.f1 AUBES1951 597 (GA) 10 CCACCCCTCCCTTTGTTTAT GACCTGCTGAGATATGGAGGAGGA 55 P AU01021A1B04.r1 AUBES1952 192 (ATT) 14 GCGAACGTGCTAACCACTAA GACCCCTGTTGCTACCCGTGTT 55 P AU01021A1G02.r1 AUBES1953 324 (CA) 30 TGGCATCTTTGACTTGTGGA GACAGGATGCCTACCCATCACAG 55 P AU01021A1D05.f1 AUBES1954 92 (CA) 18 TGCCCTAGATGTGTCAGTGTG GACCAGTGCATCACAGGGCACTA 55 AU01021B1A04.f1 AUBES1955 541 (TTA) 6 GGAACTCGCTGAGCCTTTTT GACCCACACATGCTTCCAATGAG 55 P AU01021B2D08.f1 AUBES1956 200 (CA) 20 CCCAGTCCAAAGGCATACAC GACCAACCGATTGCAGGGTACA 55 P AU01022A2H04.r1 AUBES1957 193 (TA) 22 CCCCTCGATCTATGCTCACT GACTTTGCATGGTACCTTCATGG 55 P AU01022B1F05.f1 AUBES1958 404 (CA) 11 TCCGACCATATTGTGTGTGC GACGTGTTGGCAAGGTAAGAAGACC 55 AU01022B2B02.f1 AUBES1959 279 (GT) 11 GCGATGTTCTTCTGGGTTTC GACGCAGCTGCCTATCTGCATTT 55 P AU01023A1D10.r1 AUBES1960 72 (AC) 35 AGCCACCAGAAGGCTAAAGG 55 P 60 GACTGAATTAGCCACGACAGACAA AU01023B2D05.f1 AUBES1961 386 (AG) 15 CCATCATGTCTTGCATGCTC GACACAACCACTGGCCAATCTTC 55 NP AU01026A1F07.f1 AUBES1962 457 (AAG) 8 GTAAGGAGCACGGGAATGAA GACCTCCGTTACGCACAGAACAA 60 AU01026B1C03.f1 AUBES1963 200 (AT) 10 GAATACTTTCCGGATGCACTG GACTCCTGCTCTTGCTGTCTTGA 55 P AU01027B1G12.f1 AUBES1964 537 (CA) 11 GCTCCCCATCTCTCTGTTTG GACTCCTGTTTTCCCTTCACAGC 55 P AU01028B1B01.r1 AUBES1965 51 (ATA) 20 CCCAACAAATGGCAGGTACT GACTATGGCCAAGGCCGATGTAT 55 P AU01029A1B01.f1 AUBES1966 404 (GATG) 5 GCCAGCTCATGAAGTGTTTG GACAATGCTCCCTGGGGTAGG 60 AU01029B2G12.f1 AUBES1967 363 (TC) 21 CCCTACTATTTTAACAGGGGTGTG GACTGGTCACAAGTCCTCACAGG 60 P AU01030A1F10.r1 AUBES1968 444 (TAA) 22 CGCATTGAGCAACTGTGTCT GACGCCTGGTTTTACGCTGACAT 55 P AU01030B2A05.r1 AUBES1969 370 (TG) 12 AAGGCGCGAGTCTAGACAGA GACCAGCTCGGTGTTGTTGTAGC 55 P AU01032A1H06.f1 AUBES1970 125 (TGTT) 5 GGTGTGAGTCCATACCAGCA GACCAGTCGAGCCTGGTGAAGTA 55 P AU01032B1D02.f1 AUBES1971 321 (TA) 32 CCAGTCCGACATAGTGAGGA GACCCACCATGTGCCCAGTCTAT 55 P