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Biobambam: tools for read pair collation based algorithms on BAM files

ABSTRACT: Background Sequence alignment data is often ordered by coordinate (id of the reference sequence plus position on the sequence where the fragment was mapped) when stored in BAM files, as this simplifies the extraction of variants between the mapped data and the reference or of variants within the mapped data. In this order paired reads are usually separated in the file, which complicates some other applications like duplicate marking or conversion to the FastQ format which require to access the full information of the pairs. Results In this paper we introduce biobambam, a set of tools based on the efficient collation of alignments in BAM files by read name. The employed collation algorithm avoids time and space consuming sorting of alignments by read name where this is possible without using more than a specified amount of main memory. Using this algorithm tasks like duplicate marking in BAM files and conversion of BAM files to the FastQ format can be performed very efficiently with limited resources. We also make the collation algorithm available in the form of an API for other projects. This API is part of the libmaus package. Conclusions In comparison with previous approaches to problems involving the collation of alignments by read name like the BAM to FastQ or duplication marking utilities our approach can often perform an equivalent task more efficiently in terms of the required main memory and run-time. Our BAM to FastQ conversion is faster than all widely known alternatives including Picard and bamUtil. Our duplicate marking is about as fast as the closest competitor bamUtil for small data sets and faster than all known alternatives on large and complex data sets.

SUBMITTER: Tischler G

PROVIDER: S-EPMC4075596 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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BamSnap: a lightweight viewer for sequencing reads in BAM files.

Project description:SummaryDespite the improvement in variant detection algorithms, visual inspection of the read-level data remains an essential step for accurate identification of variants in genome analysis. We developed BamSnap, an efficient BAM file viewer utilizing a graphics library and BAM indexing. In contrast to existing viewers, BamSnap can generate high-quality snapshots rapidly, with customized tracks and layout. As an example, we produced read-level images at 1000 genomic loci for >2500 whole-genomes.Availability and implementationBamSnap is freely available at https://github.com/parklab/bamsnap.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC8055225 | biostudies-literature

SAMMate: a GUI tool for processing short read alignments in SAM/BAM format.

Project description:BACKGROUND:Next Generation Sequencing (NGS) technology generates tens of millions of short reads for each DNA/RNA sample. A key step in NGS data analysis is the short read alignment of the generated sequences to a reference genome. Although storing alignment information in the Sequence Alignment/Map (SAM) or Binary SAM (BAM) format is now standard, biomedical researchers still have difficulty accessing this information. RESULTS:We have developed a Graphical User Interface (GUI) software tool named SAMMate. SAMMate allows biomedical researchers to quickly process SAM/BAM files and is compatible with both single-end and paired-end sequencing technologies. SAMMate also automates some standard procedures in DNA-seq and RNA-seq data analysis. Using either standard or customized annotation files, SAMMate allows users to accurately calculate the short read coverage of genomic intervals. In particular, for RNA-seq data SAMMate can accurately calculate the gene expression abundance scores for customized genomic intervals using short reads originating from both exons and exon-exon junctions. Furthermore, SAMMate can quickly calculate a whole-genome signal map at base-wise resolution allowing researchers to solve an array of bioinformatics problems. Finally, SAMMate can export both a wiggle file for alignment visualization in the UCSC genome browser and an alignment statistics report. The biological impact of these features is demonstrated via several case studies that predict miRNA targets using short read alignment information files. CONCLUSIONS:With just a few mouse clicks, SAMMate will provide biomedical researchers easy access to important alignment information stored in SAM/BAM files. Our software is constantly updated and will greatly facilitate the downstream analysis of NGS data. Both the source code and the GUI executable are freely available under the GNU General Public License at http://sammate.sourceforge.net.

| S-EPMC3027120 | biostudies-literature

Technology dictates algorithms: recent developments in read alignment.

Project description:Aligning sequencing reads onto a reference is an essential step of the majority of genomic analysis pipelines. Computational algorithms for read alignment have evolved in accordance with technological advances, leading to today's diverse array of alignment methods. We provide a systematic survey of algorithmic foundations and methodologies across 107 alignment methods, for both short and long reads. We provide a rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read alignment. We discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology.

| S-EPMC8390189 | biostudies-literature

Jupyter notebook-based tools for building structured datasets from the Sequence Read Archive.

Project description:The Sequence Read Archive (SRA) is a large public repository that stores raw next-generation sequencing data from thousands of diverse scientific investigations. Despite its promise, reuse and re-analysis of SRA data has been challenged by the heterogeneity and poor quality of the metadata that describe its biological samples. Recently, the MetaSRA project standardized these metadata by annotating each sample with terms from biomedical ontologies. In this work, we present a pair of Jupyter notebook-based tools that utilize the MetaSRA for building structured datasets from the SRA in order to facilitate secondary analyses of the SRA's human RNA-seq data. The first tool, called the Case-Control Finder, finds suitable case and control samples for a given disease or condition where the cases and controls are matched by tissue or cell type. The second tool, called the Series Finder, finds ordered sets of samples for the purpose of addressing biological questions pertaining to changes over a numerical property such as time. These tools were the result of a three-day-long NCBI Codeathon in March 2019 held at the University of North Carolina at Chapel Hill.

| S-EPMC7445559 | biostudies-literature

Evaluation of three read-depth based CNV detection tools using whole-exome sequencing data.

Project description:BackgroundWhole exome sequencing (WES) has been widely accepted as a robust and cost-effective approach for clinical genetic testing of small sequence variants. Detection of copy number variants (CNV) within WES data have become possible through the development of various algorithms and software programs that utilize read-depth as the main information. The aim of this study was to evaluate three commonly used, WES read-depth based CNV detection programs using high-resolution chromosomal microarray analysis (CMA) as a standard.MethodsPaired CMA and WES data were acquired for 45 samples. A total of 219 CNVs (size ranged from 2.3 kb - 35 mb) identified on three CMA platforms (Affymetrix, Agilent and Illumina) were used as standards. CNVs were called from WES data using XHMM, CoNIFER, and CNVnator with modified settings.ResultsAll three software packages detected an elevated proportion of small variants (< 20 kb) compared to CMA. XHMM and CoNIFER had poor detection sensitivity (22.2 and 14.6%), which correlated with the number of capturing probes involved. CNVnator detected most variants and had better sensitivity (87.7%); however, suffered from an overwhelming detection of small CNVs below 20 kb, which required further confirmation. Size estimation of variants was exaggerated by CNVnator and understated by XHMM and CoNIFER.ConclusionLow concordances of CNV, detected by three different read-depth based programs, indicate the immature status of WES-based CNV detection. Low sensitivity and uncertain specificity of WES-based CNV detection in comparison with CMA based CNV detection suggests that CMA will continue to play an important role in detecting clinical grade CNV in the NGS era, which is largely based on WES.

| S-EPMC5569469 | biostudies-literature

Transformation of alignment files improves performance of variant callers for long-read RNA sequencing data.

Project description:Long-read RNA sequencing (lrRNA-seq) produces detailed information about full-length transcripts, including novel and sample-specific isoforms. Furthermore, there is an opportunity to call variants directly from lrRNA-seq data. However, most state-of-the-art variant callers have been developed for genomic DNA. Here, there are two objectives: first, we perform a mini-benchmark on GATK, DeepVariant, Clair3, and NanoCaller primarily on PacBio Iso-Seq, data, but also on Nanopore and Illumina RNA-seq data; second, we propose a pipeline to process spliced-alignment files, making them suitable for variant calling with DNA-based callers. With such manipulations, high calling performance can be achieved using DeepVariant on Iso-seq data.

| S-EPMC10123983 | biostudies-literature

Combining probabilistic alignments with read pair information improves accuracy of split-alignments.

Project description:Motivation:Split-alignments provide base-pair-resolution evidence of genomic rearrangements. In practice, they are found by first computing high-scoring local alignments, parts of which are then combined into a split-alignment. This approach is challenging when aligning a short read to a large and repetitive reference, as it tends to produce many spurious local alignments leading to ambiguities in identifying the correct split-alignment. This problem is further exacerbated by the fact that rearrangements tend to occur in repeat-rich regions. Results:We propose a split-alignment technique that combats the issue of ambiguous alignments by combining information from probabilistic alignment with positional information from paired-end reads. We demonstrate that our method finds accurate split-alignments, and that this translates into improved performance of variant-calling tools that rely on split-alignments. Availability and implementation:An open-source implementation is freely available at: https://bitbucket.org/splitpairedend/last-split-pe. Supplementary information:Supplementary data are available at Bioinformatics online.

| S-EPMC6198854 | biostudies-other

Coding algorithms for defining Charlson and Elixhauser co-morbidities in Read-coded databases.

Project description:BACKGROUND:Comorbidity measures, such as the Charlson Comorbidity Index (CCI) and Elixhauser Method (EM), are frequently used for risk-adjustment by healthcare researchers. This study sought to create CCI and EM lists of Read codes, which are standard terminology used in some large primary care databases. It also aimed to describe and compare the predictive properties of the CCI and EM amongst patients with hip fracture (and matched controls) in a large primary care administrative dataset. METHODS:Two researchers independently screened 111,929 individual Read codes to populate the 17 CCI and 31 EM comorbidity categories. Patients with hip fractures were identified (together with age- and sex-matched controls) from UK primary care practices participating in the Clinical Practice Research Datalink (CPRD). The predictive properties of both comorbidity measures were explored in hip fracture and control populations using logistic regression models fitted with 30- and 365-day mortality as the dependent variables together with tests of equality for Receiver Operating Characteristic (ROC) curves. RESULTS:There were 5832 CCI and 7156 EM comorbidity codes. The EM improved the ability of a logistic regression model (using age and sex as covariables) to predict 30-day mortality (AUROC 0.744 versus 0.686). The EM alone also outperformed the CCI (0.696 versus 0.601). Capturing comorbidities over a prolonged period only modestly improved the predictive value of either index: EM 1-year look-back 0.645 versus 5-year 0.676 versus complete record 0.695 and CCI 0.574 versus 0.591 versus 0.605. CONCLUSIONS:The comorbidity code lists may be used by future researchers to calculate CCI and EM using records from Read coded databases. The EM is preferable to the CCI but only marginal gains should be expected from incorporating comorbidities over a period longer than 1 year.

| S-EPMC6554904 | biostudies-other

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