Browse
Submit Data
Databases
API
Help

Dataset Information

29 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

BatAlign: an incremental method for accurate alignment of sequencing reads.

ABSTRACT: Structural variations (SVs) play a crucial role in genetic diversity. However, the alignments of reads near/across SVs are made inaccurate by the presence of polymorphisms. BatAlign is an algorithm that integrated two strategies called 'Reverse-Alignment' and 'Deep-Scan' to improve the accuracy of read-alignment. In our experiments, BatAlign was able to obtain the highest F-measures in read-alignments on mismatch-aberrant, indel-aberrant, concordantly/discordantly paired and SV-spanning data sets. On real data, the alignments of BatAlign were able to recover 4.3% more PCR-validated SVs with 73.3% less callings. These suggest BatAlign to be effective in detecting SVs and other polymorphic-variants accurately using high-throughput data. BatAlign is publicly available at https://goo.gl/a6phxB.

SUBMITTER: Lim JQ

PROVIDER: S-EPMC4652746 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

BatAlign: an incremental method for accurate alignment of sequencing reads.

Lim Jing-Quan JQ Tennakoon Chandana C Guan Peiyong P Sung Wing-Kin WK

Nucleic acids research 20150713 16

Structural variations (SVs) play a crucial role in genetic diversity. However, the alignments of reads near/across SVs are made inaccurate by the presence of polymorphisms. BatAlign is an algorithm that integrated two strategies called 'Reverse-Alignment' and 'Deep-Scan' to improve the accuracy of read-alignment. In our experiments, BatAlign was able to obtain the highest F-measures in read-alignments on mismatch-aberrant, indel-aberrant, concordantly/discordantly paired and SV-spanning data set ...[more]

PMID: 26170239

Similar Datasets

Rapid and accurate alignment of nucleotide conversion sequencing reads with HISAT-3N.

Project description:Sequencing technologies using nucleotide conversion techniques such as cytosine to thymine in bisulfite-seq and thymine to cytosine in SLAM seq are powerful tools to explore the chemical intricacies of cellular processes. To date, no one has developed a unified methodology for aligning converted sequences and consolidating alignment of these technologies in one package. In this paper, we describe hierarchical indexing for spliced alignment of transcripts-3 nucleotides (HISAT-3N), which can rapidly and accurately align sequences consisting of any nucleotide conversion by leveraging the powerful hierarchical index and repeat index algorithms originally developed for the HISAT software. Tests on real and simulated data sets show that HISAT-3N is faster than other modern systems, with greater alignment accuracy, higher scalability, and smaller memory requirements. HISAT-3N therefore becomes an ideal aligner when used with converted sequence technologies.

| S-EPMC8256862 | biostudies-literature

3âREADS+, a sensitive and accurate method for 3â end sequencing of polyadenylated RNA

Project description:Sequencing of the 3â end of poly(A)+ RNA identifies cleavage and polyadenylation sites (pAs) and measures transcript expression. We previously developed a method, 3â region extraction and deep sequencing (3âREADS), to address mispriming issues that often plague 3â end sequencing. Here we report a new version, named 3âREADS+, which has vastly improved accuracy and sensitivity. Using a special locked nucleic acid oligo to capture poly(A)+ RNA and to remove bulk of the poly(A) tail, 3âREADS+ generates RNA fragments with an optimal number of terminal As that balance data quality and detection of genuine pAs. With improved RNA ligation steps for efficiency, the method shows much higher sensitivity (over two orders of magnitude) compared to the previous version. Using 3âREADS+, we have uncovered a sizable fraction of previously overlooked pAs located next to or within a stretch of adenylate residues in human genes, and more accurately assessed the frequency of alternative cleavage and polyadenylation (APA) in HeLa cells (~50%). 3âREADS+ will be a useful tool to accurately study APA and to analyze gene expression by 3â end counting, especially when the amount of input total RNA is limited. Nine 3'READS+ libraries were made with different amounts (100 ng, 200 ng, 400 ng, 1000 ng, 5000 ng, 15000 ng) of input Hela RNA.

2016-08-14 | E-GEOD-84170 | biostudies-arrayexpress

FastGT: an alignment-free method for calling common SNVs directly from raw sequencing reads.

Project description:We have developed a computational method that counts the frequencies of unique k-mers in FASTQ-formatted genome data and uses this information to infer the genotypes of known variants. FastGT can detect the variants in a 30x genome in less than 1 hour using ordinary low-cost server hardware. The overall concordance with the genotypes of two Illumina "Platinum" genomes is 99.96%, and the concordance with the genotypes of the Illumina HumanOmniExpress is 99.82%. Our method provides k-mer database that can be used for the simultaneous genotyping of approximately 30 million single nucleotide variants (SNVs), including >23,000 SNVs from Y chromosome. The source code of FastGT software is available at GitHub (https://github.com/bioinfo-ut/GenomeTester4/).

| S-EPMC5451431 | biostudies-literature

3'READS+, a sensitive and accurate method for 3' end sequencing of polyadenylated RNA.

Project description:Sequencing of the 3' end of poly(A)(+) RNA identifies cleavage and polyadenylation sites (pAs) and measures transcript expression. We previously developed a method, 3' region extraction and deep sequencing (3'READS), to address mispriming issues that often plague 3' end sequencing. Here we report a new version, named 3'READS+, which has vastly improved accuracy and sensitivity. Using a special locked nucleic acid oligo to capture poly(A)(+) RNA and to remove the bulk of the poly(A) tail, 3'READS+ generates RNA fragments with an optimal number of terminal A's that balance data quality and detection of genuine pAs. With improved RNA ligation steps for efficiency, the method shows much higher sensitivity (over two orders of magnitude) compared to the previous version. Using 3'READS+, we have uncovered a sizable fraction of previously overlooked pAs located next to or within a stretch of adenylate residues in human genes and more accurately assessed the frequency of alternative cleavage and polyadenylation (APA) in HeLa cells (∼50%). 3'READS+ will be a useful tool to accurately study APA and to analyze gene expression by 3' end counting, especially when the amount of input total RNA is limited.

| S-EPMC5029459 | biostudies-literature

3’READS+, a sensitive and accurate method for 3’ end sequencing of polyadenylated RNA

Project description:Sequencing of the 3’ end of poly(A)+ RNA identifies cleavage and polyadenylation sites (pAs) and measures transcript expression. We previously developed a method, 3’ region extraction and deep sequencing (3’READS), to address mispriming issues that often plague 3’ end sequencing. Here we report a new version, named 3’READS+, which has vastly improved accuracy and sensitivity. Using a special locked nucleic acid oligo to capture poly(A)+ RNA and to remove bulk of the poly(A) tail, 3’READS+ generates RNA fragments with an optimal number of terminal As that balance data quality and detection of genuine pAs. With improved RNA ligation steps for efficiency, the method shows much higher sensitivity (over two orders of magnitude) compared to the previous version. Using 3’READS+, we have uncovered a sizable fraction of previously overlooked pAs located next to or within a stretch of adenylate residues in human genes, and more accurately assessed the frequency of alternative cleavage and polyadenylation (APA) in HeLa cells (~50%). 3’READS+ will be a useful tool to accurately study APA and to analyze gene expression by 3’ end counting, especially when the amount of input total RNA is limited.

2016-08-14 | GSE84170 | GEO

Efficient alignment of pyrosequencing reads for re-sequencing applications.

Project description:BACKGROUND: Over the past few years, new massively parallel DNA sequencing technologies have emerged. These platforms generate massive amounts of data per run, greatly reducing the cost of DNA sequencing. However, these techniques also raise important computational difficulties mostly due to the huge volume of data produced, but also because of some of their specific characteristics such as read length and sequencing errors. Among the most critical problems is that of efficiently and accurately mapping reads to a reference genome in the context of re-sequencing projects. RESULTS: We present an efficient method for the local alignment of pyrosequencing reads produced by the GS FLX (454) system against a reference sequence. Our approach explores the characteristics of the data in these re-sequencing applications and uses state of the art indexing techniques combined with a flexible seed-based approach, leading to a fast and accurate algorithm which needs very little user parameterization. An evaluation performed using real and simulated data shows that our proposed method outperforms a number of mainstream tools on the quantity and quality of successful alignments, as well as on the execution time. CONCLUSIONS: The proposed methodology was implemented in a software tool called TAPyR--Tool for the Alignment of Pyrosequencing Reads--which is publicly available from http://www.tapyr.net.

| S-EPMC3118166 | biostudies-literature

INC-Seq: accurate single molecule reads using nanopore sequencing.

Project description:BACKGROUND:Nanopore sequencing provides a rapid, cheap and portable real-time sequencing platform with the potential to revolutionize genomics. However, several applications are limited by relatively high single-read error rates (>10 %), including RNA-seq, haplotype sequencing and 16S sequencing. RESULTS:We developed the Intramolecular-ligated Nanopore Consensus Sequencing (INC-Seq) as a strategy for obtaining long and accurate nanopore reads, starting with low input DNA. Applying INC-Seq for 16S rRNA-based bacterial profiling generated full-length amplicon sequences with a median accuracy >97 %. CONCLUSIONS:INC-Seq reads enabled accurate species-level classification, identification of species at 0.1 % abundance and robust quantification of relative abundances, providing a cheap and effective approach for pathogen detection and microbiome profiling on the MinION system.

| S-EPMC4970289 | biostudies-literature

Ultra-accurate microbial amplicon sequencing with synthetic long reads.

Project description:BackgroundOut of the many pathogenic bacterial species that are known, only a fraction are readily identifiable directly from a complex microbial community using standard next generation DNA sequencing. Long-read sequencing offers the potential to identify a wider range of species and to differentiate between strains within a species, but attaining sufficient accuracy in complex metagenomes remains a challenge.MethodsHere, we describe and analytically validate LoopSeq, a commercially available synthetic long-read (SLR) sequencing technology that generates highly accurate long reads from standard short reads.ResultsLoopSeq reads are sufficiently long and accurate to identify microbial genes and species directly from complex samples. LoopSeq perfectly recovered the full diversity of 16S rRNA genes from known strains in a synthetic microbial community. Full-length LoopSeq reads had a per-base error rate of 0.005%, which exceeds the accuracy reported for other long-read sequencing technologies. 18S-ITS and genomic sequencing of fungal and bacterial isolates confirmed that LoopSeq sequencing maintains that accuracy for reads up to 6 kb in length. LoopSeq full-length 16S rRNA reads could accurately classify organisms down to the species level in rinsate from retail meat samples, and could differentiate strains within species identified by the CDC as potential foodborne pathogens.ConclusionsThe order-of-magnitude improvement in length and accuracy over standard Illumina amplicon sequencing achieved with LoopSeq enables accurate species-level and strain identification from complex- to low-biomass microbiome samples. The ability to generate accurate and long microbiome sequencing reads using standard short read sequencers will accelerate the building of quality microbial sequence databases and removes a significant hurdle on the path to precision microbial genomics. Video abstract.

| S-EPMC8179091 | biostudies-literature

Next generation sequencing reads comparison with an alignment-free distance.

Project description:Next Generation Sequencing (NGS) machines extract from a biological sample a large number of short DNA fragments (reads). These reads are then used for several applications, e.g., sequence reconstruction, DNA assembly, gene expression profiling, mutation analysis.We propose a method to evaluate the similarity between reads. This method does not rely on the alignment of the reads and it is based on the distance between the frequencies of their substrings of fixed dimensions (k-mers). We compare this alignment-free distance with the similarity measures derived from two alignment methods: Needleman-Wunsch and Blast. The comparison is based on a simple assumption: the most correct distance is obtained by knowing in advance the reference sequence. Therefore, we first align the reads on the original DNA sequence, compute the overlap between the aligned reads, and use this overlap as an ideal distance. We then verify how the alignment-free and the alignment-based distances reproduce this ideal distance. The ability of correctly reproducing the ideal distance is evaluated over samples of read pairs from Saccharomyces cerevisiae, Escherichia coli, and Homo sapiens. The comparison is based on the correctness of threshold predictors cross-validated over different samples.We exhibit experimental evidence that the proposed alignment-free distance is a potentially useful read-to-read distance measure and performs better than the more time consuming distances based on alignment.Alignment-free distances may be used effectively for reads comparison, and may provide a significant speed-up in several processes based on NGS sequencing (e.g., DNA assembly, reads classification).

| S-EPMC4265526 | biostudies-literature

Alignment-free sequence comparison based on next-generation sequencing reads.

Project description:Next-generation sequencing (NGS) technologies have generated enormous amounts of shotgun read data, and assembly of the reads can be challenging, especially for organisms without template sequences. We study the power of genome comparison based on shotgun read data without assembly using three alignment-free sequence comparison statistics, D(2), D(*)(2) and D(s)(2), both theoretically and by simulations. Theoretical formulas for the power of detecting the relationship between two sequences related through a common motif model are derived. It is shown that both D(*)(2) and D(s)(2), outperform D(2) for detecting the relationship between two sequences based on NGS data. We then study the effects of length of the tuple, read length, coverage, and sequencing error on the power of D(*)(2) and D(s)(2). Finally, variations of these statistics, d(2), d(*)(2) and d(s)(2), respectively, are used to first cluster five mammalian species with known phylogenetic relationships, and then cluster 13 tree species whose complete genome sequences are not available using NGS shotgun reads. The clustering results using d(s)(2) are consistent with biological knowledge for the 5 mammalian and 13 tree species, respectively. Thus, the statistic d(s)(2) provides a powerful alignment-free comparison tool to study the relationships among different organisms based on NGS read data without assembly.

| S-EPMC3581251 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data