Short Read Clustering Research Articles

Characterization of Satellite DNAs in Squirrel Monkeys genus Saimiri (Cebidae, Platyrrhini)

The genus Saimiri is a decades-long taxonomic and phylogenetic puzzle to which cytogenetics has contributed crucial data. All Saimiri species apparently have a diploid number of 2n = 44 but vary in the number of chromosome arms. Repetitive sequences such as satellite DNAs are potentially informative cytogenetic markers because they display high evolutionary rates. Our goal is to increase the pertinent karyological data by more fully characterizing satellite DNA sequences in the Saimiri genus. We were able to identify two abundant satellite DNAs, alpha (~340 bp) and CapA (~1,500 bp), from short-read clustering of sequencing datasets from S. boliviensis. The alpha sequences comprise about 1% and the CapA 2.2% of the S. boliviensis genome. We also mapped both satellite DNAs in S. boliviensis, S. sciureus, S. vanzolinii, and S. ustus. The alpha has high interspecific repeat homogeneity and was mapped to the centromeres of all analyzed species. CapA is associated with non-pericentromeric heterochromatin and its distribution varies among Saimiri species. We conclude that CapA genomic distribution and its pervasiveness across Platyrrhini makes it an attractive cytogenetic marker for Saimiri and other New World monkeys.

Deconvolute individual genomes from metagenome sequences through short read clustering.

Metagenome assembly from short next-generation sequencing data is a challenging process due to its large scale and computational complexity. Clustering short reads by species before assembly offers a unique opportunity for parallel downstream assembly of genomes with individualized optimization. However, current read clustering methods suffer either false negative (under-clustering) or false positive (over-clustering) problems. Here we extended our previous read clustering software, SpaRC, by exploiting statistics derived from multiple samples in a dataset to reduce the under-clustering problem. Using synthetic and real-world datasets we demonstrated that this method has the potential to cluster almost all of the short reads from genomes with sufficient sequencing coverage. The improved read clustering in turn leads to improved downstream genome assembly quality.

Satellite DNA landscapes after allotetraploidization of quinoa (Chenopodium quinoa) reveal unique A and B subgenomes.

If two related plant species hybridize, their genomes may be combined and duplicated within a single nucleus, thereby forming an allotetraploid. How the emerging plant balances two co-evolved genomes is still a matter of ongoing research. Here, we focus on satellite DNA (satDNA), the fastest turn-over sequence class in eukaryotes, aiming to trace its emergence, amplification, and loss during plant speciation and allopolyploidization. As a model, we used Chenopodium quinoa Willd. (quinoa), an allopolyploid crop with 2n=4x=36 chromosomes. Quinoa originated by hybridization of an unknown female American Chenopodium diploid (AA genome) with an unknown male Old World diploid species (BB genome), dating back 3.3-6.3million years. Applying short read clustering to quinoa (AABB), C.pallidicaule (AA), and C.suecicum (BB) whole genome shotgun sequences, we classified their repetitive fractions, and identified and characterized seven satDNA families, together with the 5S rDNA model repeat. We show unequal satDNA amplification (two families) and exclusive occurrence (four families) in the AA and BB diploids by read mapping as well as Southern, genomic, and fluorescent in situ hybridization. Whereas the satDNA distributions support C. suecicum as possible parental species, we were able to exclude C. pallidicaule as progenitor due to unique repeat profiles.Using quinoa long reads and scaffolds, we detected only limited evidence of intergenomic homogenization of satDNA after allopolyploidization, but were able to exclude dispersal of 5S rRNA genes between subgenomes. Our results exemplify the complex route of tandem repeat evolution through Chenopodium speciation and allopolyploidization, and may provide sequence targets for the identification of quinoa's progenitors.

Improvement of detection performance of fusion genes from RNA-seq data by clustering short reads.

Fusion genes are involved in cancer, and their detection using RNA-Seq is insufficient given the relatively short reading length. Therefore, we proposed a shifted short-read clustering (SSC) method, which focuses on overlapping reads from the same loci and extends them as a representative sequence. To verify their usefulness, we applied the SSC method to RNA-Seq data from four types of cell lines (BT-474, MCF-7, SKBR-3, and T-47D). As the slide width of the SSC method increased to one, two, five, or ten bases, the read length was extended from 201 bases to 217 (108%), 234 (116%), 282 (140%), or 317 (158%) bases, respectively. Furthermore, fusion genes were investigated using STAR-Fusion, a fusion gene detection tool, with and without the SSC method. When one base was shifted by the SSC method, the reads mapped to multiple loci decreased from 9.7% to 4.6%, and the sensitivity of the fusion gene was improved from 47% to 54% on average (BT-474: from 48% to 57%, MCF-7: 49% to 53%, SKBR-3: 50% to 57%, and T-47D: 43% to 50%) compared with original data. When the reads are shifted more, the positive predictive value was also improved. The SSC method could be an effective method for fusion gene detection.

Efficient frequency-based de novo short-read clustering for error trimming in next-generation sequencing

Novel massively parallel sequencing technologies provide highly detailed structures of transcriptomes and genomes by yielding deep coverage of short reads, but their utility is limited by inadequate sequencing quality and short-read lengths. Sequencing-error trimming in short reads is therefore a vital process that could improve the rate of successful reference mapping and polymorphism detection. Toward this aim, we herein report a frequency-based, de novo short-read clustering method that organizes erroneous short sequences originating in a single abundant sequence into a tree structure; in this structure, each "child" sequence is considered to be stochastically derived from its more abundant "parent" sequence with one mutation through sequencing errors. The root node is the most frequently observed sequence that represents all erroneous reads in the entire tree, allowing the alignment of the reliable representative read to the genome without the risk of mapping erroneous reads to false-positive positions. This method complements base calling and the error correction of making direct alignments with the reference genome, and is able to improve the overall accuracy of short-read alignment by consulting the inherent relationships among the entire set of reads. The algorithm runs efficiently with a linear time complexity. In addition, an error rate evaluation model can be derived from bacterial artificial chromosome sequencing data obtained in the same run as a control. In two clustering experiments using small RNA and 5'-end mRNA reads data sets, we confirmed a remarkable increase ( approximately 5%) in the percentage of short reads aligned to the reference sequence.