The Sequence Alignment/Map format and SAMtools
Open accessAbstract
SUMMARY: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. AVAILABILITY: http://samtools.sourceforge.net.
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Mapping reads to specific genes is relatively straightforward with available bioinformatics tools, but using these tools to distinguish between reads derived from alternative alleles of the same gene remains challenging.
“Mapping reads to specific genes is relatively straightforward with the bioinformatics tools available today [10-13], but using these tools to distinguish between reads derived from alternative alleles of the same gene remains challenging [9].”
From Sources of bias in measures of allele-specific expression derived from RNA-seq data aligned to a single reference genome (2013) · cites this paper as [13]
Droplet single-cell RNA sequencing has made it possible to profile transcriptomes rapidly and at large scale.
“Droplet single cell RNA-sequencing (dscRNA-seq) has increased substantially the throughput of single cell capture and library preparation1, 10, enabling the simultaneous profiling of thousands of cells.”
From Multiplexed droplet single-cell RNA-sequencing using natural genetic variation (2018) · cites this paper as [10]
The Introduction and Methods passages (pages not supplied) describe the SAM alignment format and SAMtools, not droplet single-cell RNA sequencing. Searches for droplet sequencing, cell-capture throughput, and profiling thousands of cells found no passage addressing the claim. (LLM, conf=0.76)
Experimental — accuracy not yet measuredSimultaneously demultiplexing and detecting doublets from more than two individuals has not been possible.
“While strategies to demultiplex cells from different species1, 10, 17 or host and graft samples17 have been reported, simultaneously demultiplexing and detecting doublets from more than two individuals has not been possible.”
From Multiplexed droplet single-cell RNA-sequencing using natural genetic variation (2018) · cites this paper as [10]
The Introduction and Methods sections describe the SAM/BAM alignment format and SAMtools, not cell demultiplexing. Searches for simultaneous demultiplexing, doublet detection, and more than two individuals found no passage addressing the claimed limitation. (LLM, conf=0.76)
Experimental — accuracy not yet measuredAfter TMM and limma-voom normalization, samples A and B were well separated by multidimensional scaling, and two replicates with abnormal GC distributions were separated from other sample A replicates at dimension 2.
“After the Trimmmed Mean of M-values (TMM)55 and limma-voom normalization, we found that samples A and B were well distinguished by multidimensional scaling (Supplementary Fig. 21d), and that the two replicates with abnormal GC content distributions (PGM1.A.4 and PGM2.A.2) were separated from the other replicates of sample A at dimension 2.”
From Detecting and correcting systematic variation in large-scale RNA sequencing data (2014) · cites this paper as [55]
For ChIP-seq alignments, only uniquely mapped reads were kept and duplicate reads were removed.
“Only uniquely mapped reads were retained, and the alignments were subsequently filtered using the SAMtools package (v0.1.19)88 to remove duplicate reads.”
From Epigenetic regulation of brain region-specific microglia clearance activity (2018) · cites this paper as [88]
The potential of whole genome imputation to infer diploid genotypes from low-coverage genomes was evaluated by randomly subsampling high-coverage genomes (NE1 and BR2) to coverages from 0.1 to 5×.
“We evaluated the potential of whole genome imputation to infer diploid genotypes from low-coverage genomes by randomly subsampling our high-coverage genomes (NE1 and BR2) to a series of coverages ranging from 0.1 to 5 × using SAMtools45”
From Genome flux and stasis in a five millennium transect of European prehistory (2014) · cites this paper as [45]
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- Authors
- Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup
- Affiliations
- Boston College (US)
- 1 Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK, 2Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA, 3Beijing Institute of Genomics, Chinese Academy of Science, Beijing 100029, China, 4Department of Computer Science, University of California Los Angeles, Los Angeles, CA 90095, 5Department of Biology, Boston College, Chestnut Hill, MA 02467, 6Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA and 7http://1000genomes.org
- Corresponding author
- Durbin, Richard
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2009-06-08
- Volume
- 25
- Issue
- 16
- Pages
- 2078-2079
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC2723002
- OpenAlex
- W2108234281
- Cited by
- 62,041
- References
- 4
- Citations by year
- 2026: 4515 · 2025: 5629 · 2024: 6225 · 2023: 5854 · 2022: 6476 · 2021: 7180 · 2020: 5531 · 2019: 4365 · 2018: 3577 · 2017: 3034 · 2016: 2584 · 2015: 2289 · 2014: 1917 · 2013: 1350 · 2012: 853
- FWCI
- 194.13
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Funders
- Wellcome Trust (077192/Z/05/Z)
- National Institutes of Health (077192)
- National Human Genome Research Institute (U54HG002750, R01HG004719)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Computer Science › Artificial Intelligence › Algorithms and Data Compression
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genetics, Bioinformatics, and Biomedical Research
- Keywords
- SAMtools, variant calling, 1000 Genomes Project, long reads
- MeSH terms
- Algorithms, Base Sequence, Molecular Sequence Data, Software, Sequence Alignment, Genome, Sequence Analysis, DNA, Computational Biology, Genomics
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