Cited by 876Genomics and Phylogenetic StudiesAlgorithms and Data CompressionFractal and DNA sequence analysis

The DOI matches its Crossref record; 73 of the 74 references it cites were found in a registry or an index.

A survey of sequence alignment algorithms for next-generation sequencing

Open access

Li, H., Homer, N.·2010·Briefings in Bioinformatics

Open this paper in CoChat — free account Open PDF

Abstract

Rapidly evolving sequencing technologies produce data on an unparalleled scale. A central challenge to the analysis of this data is sequence alignment, whereby sequence reads must be compared to a reference. A wide variety of alignment algorithms and software have been subsequently developed over the past two years. In this article, we will systematically review the current development of these algorithms and introduce their practical applications on different types of experimental data. We come to the conclusion that short-read alignment is no longer the bottleneck of data analyses. We also consider future development of alignment algorithms with respect to emerging long sequence reads and the prospect of cloud computing.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1093/bib/bbq015

Cites

73 of 73 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Basic local alignment search tool
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669
Gapped BLAST and PSI-BLAST: a new generation of protein database search programs
Altschul, S. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.17.3389 · Cited by 73,302
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041
Self-citation
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
Self-citation
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696
Fast and accurate long-read alignment with Burrows-Wheeler transform.
Li, H., & Durbin, R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp698 · Cited by 12,063
Self-citation
TopHat: discovering splice junctions with RNA-Seq
Trapnell, C., Pachter, L. S., & Salzberg, S. L. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp120 · Cited by 11,682
Identification of common molecular subsequences
Smith, T. F., & Waterman, M. S. (1981) · Journal of Molecular Biology · doi:10.1016/0022-2836(81)90087-5 · Cited by 9,962
<tt>BLAT</tt> —The <tt>BLAST</tt> -Like Alignment Tool
Kent, W. J. (2002) · Genome Research · doi:10.1101/gr.229202 · Cited by 8,480

Flagged first, then by citations

Show all 74 references

1 of the 74 references couldn’t be matched to a known paper.

Research works printed but not linked: 1

1985, THEORETICAL COMPUTER SCIENCE, 40, 31

Sorted by citations

Items 101–200 of 876

Short read alignment with populations of genomes
Huang, L., Popic, V., & Batzoglou, S. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt215 · Cited by 105
AnchorWave: Sensitive alignment of genomes with high sequence diversity, extensive structural polymorphism, and whole-genome duplication
Song, B., Marco‐Sola, S., Moretó, M., Johnson, L., Buckler, E. S., & Stitzer, M. C. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2113075119 · Cited by 111
Toward reliable biomarker signatures in the age of liquid biopsies - how to standardize the small RNA-Seq workflow
Buschmann, D., Haberberger, A., Kirchner, B., Spornraft, M., Riedmaier, I., Schelling, G., & Pfaffl, M. W. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw545 · Cited by 102
High-throughput DNA sequence data compression
Zhu, Z., Zhang, Y., Ji, Z., He, S., & Yang, X. (2013) · Briefings in Bioinformatics · doi:10.1093/bib/bbt087 · Cited by 102
Autosomal dominant spondylocostal dysostosis is caused by mutation in TBX6
Sparrow, D. B., McInerney-Leo, A. M., Gucev, Z. S., Gardiner, B., Marshall, M. S., Leo, P. J., Chapman, D. L., Tasic, V. B., Shishko, A., Brown, M. A., et al. (2013) · Human Molecular Genetics · doi:10.1093/hmg/ddt012 · Cited by 101
DNA Sequencing
Dewey, F. E., Pan, S., Wheeler, M. T., Quake, S. R., & Ashley, E. A. (2012) · Circulation · doi:10.1161/circulationaha.110.972828 · Cited by 105
Recent advances in inferring viral diversity from high-throughput sequencing data
Posada-Céspedes, S., Seifert, D., & Beerenwinkel, N. (2016) · Virus Research · doi:10.1016/j.virusres.2016.09.016 · Cited by 103
An Overview of Multiple Sequence Alignments and Cloud Computing in Bioinformatics
Daugelaite, J., O’Driscoll, A., & Sleator, R. D. (2013) · ISRN Biomathematics · doi:10.1155/2013/615630 · Cited by 99
A Fast Approximate Algorithm for Mapping Long Reads to Large Reference Databases
Jain, C., Dilthey, A., Koren, S., Aluru, S., & Phillippy, A. M. (2018) · Journal of Computational Biology · doi:10.1089/cmb.2018.0036 · Cited by 98
Integrative Analysis of ChIP-Chip and ChIP-Seq Dataset
Zhu, L. J. (2013) · Methods in molecular biology · doi:10.1007/978-1-62703-607-8_8 · Cited by 98
Comparison of mapping algorithms used in high-throughput sequencing: application to Ion Torrent data
Caboche, S., Audebert, C., Lemoine, Y., & Hot, D. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-264 · Cited by 97
Genetic and Genomic Architecture of the Evolution of Resistance to Antifungal Drug Combinations
Hill, J. A., Ammar, R., Torti, D., Nislow, C., & Cowen, L. E. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003390 · Cited by 93
Recent advances in molecular marker techniques: Insight into QTL mapping, GWAS and genomic selection in plants
Zargar, S. M., Raatz, B., Sonah, H., MuslimaNazir, Bhat, J. A., Dar, Z. A., Agrawal, G. K., & Rakwal, R. (2015) · Journal of Crop Science and Biotechnology · doi:10.1007/s12892-015-0037-5 · Cited by 101
Verification and validation of bioinformatics software without a gold standard: a case study of BWA and Bowtie
Giannoulatou, E., Park, S. H., Humphreys, D. T., & Ho, J. W. K. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-s16-s15 · Cited by 94
Bioinformatics for Next Generation Sequencing Data
Magi, A., Benelli, M., Gozzini, A., Girolami, F., Torricelli, F., & Brandi, M. L. (2010) · Genes · doi:10.3390/genes1020294 · Cited by 99
DNA Sequencing Sensors: An Overview
Garrido-Cárdenas, J. A., García‐Maroto, F., Bermejo, J. A. Á., & Manzano-Agugliaro, F. R. (2017) · Sensors · doi:10.3390/s17030588 · Cited by 88
Computational Techniques for Human Genome Resequencing Using Mated Gapped Reads
Carnevali, P., Baccash, J., Halpern, A., Nazarenko, I., Nilsen, G. B., Pant, K., Ebert, J. C., Brownley, A., Morenzoni, M., Karpinchyk, V., et al. (2012) · J. Comput. Biol. · doi:10.1089/cmb.2011.0201 · Cited by 95
Whole genome sequencing as a typing tool for foodborne pathogens like Listeria monocytogenes – The way towards global harmonisation and data exchange
Lüth, S., Kleta, S., & Dahouk, S. A. (2018) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2018.01.008 · Cited by 87
BIMA V3: an aligner customized for mate pair library sequencing
Drucker, T., Johnson, S. H., Murphy, S. J., Cradic, K. W., Therneau, T. M., & Vasmatzis, G. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu078 · Cited by 85
Molecular breeding to improve guava (Psidium guajava L.): Current status and future prospective
Nimisha, S., Kherwar, D., Ajay, K. M., Singh, B., & Usha, K. (2013) · Scientia Horticulturae · doi:10.1016/j.scienta.2013.10.017 · Cited by 83
deBGA: read alignment with de Bruijn graph-based seed and extension
Liu, B., Guo, H., Brudno, M., & Wang, Y. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw371 · Cited by 83
Review of alignment and SNP calling algorithms for next-generation sequencing data
Mielczarek, M., & Szyda, J. (2015) · Journal of Applied Genetics · doi:10.1007/s13353-015-0292-7 · Cited by 83
Next generation sequencing has lower sequence coverage and poorer SNP-detection capability in the regulatory regions
Wang, W., Wei, Z., Lam, T., & Wang, J. (2011) · Scientific Reports · doi:10.1038/srep00055 · Cited by 81
Next–generation sequencing and systematics: What can a billion base pairs of DNA sequence data do for you?
Harrison, N., & Kidner, C. A. (2011) · Taxon · doi:10.1002/tax.606002 · Cited by 81
mirPRo–a novel standalone program for differential expression and variation analysis of miRNAs
Shi, J., Dong, M., Li, L., Liu, L., Luz-Madrigal, A., Tsonis, P. A., Rio‐Tsonis, K. D., & Liang, C. (2015) · Scientific Reports · doi:10.1038/srep14617 · Cited by 82
Deafness in the Genomics Era
Shearer, A., Hildebrand, M., Sloan, C. M., & Smith, R. J. H. (2011) · Hearing Research · doi:10.1016/j.heares.2011.10.001 · Cited by 73
Deep sequencing of evolving pathogen populations: applications, errors, and bioinformatic solutions
McElroy, K., Thomas, T., & Luciani, F. (2014) · Microbial Informatics and Experimentation · doi:10.1186/2042-5783-4-1 · Cited by 77
Competition between ADAR and RNAi pathways for an extensive class of RNA targets
Wu, D. S., Lamm, A. T., & Fire, A. Z. (2011) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.2129 · Cited by 77
AlignGraph: algorithm for secondary de novo genome assembly guided by closely related references
Bao, E., Jiang, T., & Girke, T. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu291 · Cited by 73
Coval: Improving Alignment Quality and Variant Calling Accuracy for Next-Generation Sequencing Data
Kosugi, S., Natsume, S., Yoshida, K., MacLean, D., Cano, L. M., Kamoun, S., & Terauchi, R. (2013) · PLoS ONE · doi:10.1371/journal.pone.0075402 · Cited by 72
High‐throughput genomics in sorghum: from whole‐genome resequencing to a SNP screening array
Bekele, W. A., Wieckhorst, S., Friedt, W., & Snowdon, R. J. (2013) · Plant Biotechnology Journal · doi:10.1111/pbi.12106 · Cited by 72
Preparation of high-quality next-generation sequencing libraries from picogram quantities of target DNAPreprint
Parkinson, N., Maslau, S., Ferneyhough, B., Zhang, G., Gregory, L., Buck, D., Ragoussis, J., Ponting, C. P., & Fischer, M. D. (2011) · Genome Research · doi:10.1101/gr.124016.111 · Cited by 71
A Next Generation Semiconductor Based Sequencing Approach for the Identification of Meat Species in DNA Mixtures
Bertolini, F., Ghionda, M. C., D’Alessandro, E., Geraci, C., Chiofalo, V., & Fontanesi, L. (2015) · PLoS ONE · doi:10.1371/journal.pone.0121701 · Cited by 74
Identification, characterization and interpretation of single‐nucleotide sequence variation in allopolyploid crop species
Kaur, S., Francki, M. G., & Forster, J. W. (2011) · Plant Biotechnology Journal · doi:10.1111/j.1467-7652.2011.00644.x · Cited by 69
Large-Scale Privacy-Preserving Mapping of Human Genomic Sequences on Hybrid Clouds.
Chen, Y., Peng, B., Wang, X., & Tang, H. (2012) · Network and Distributed System Security Symposium · Cited by 70
CUSHAW3: Sensitive and Accurate Base-Space and Color-Space Short-Read Alignment with Hybrid Seeding
Liu, Y., Popp, B., & Schmidt, B. (2014) · PLoS ONE · doi:10.1371/journal.pone.0086869 · Cited by 67
All-Food-Seq (AFS): a quantifiable screen for species in biological samples by deep DNA sequencing
Ripp, F., Krombholz, C. F., Liu, Y., Weber, M., Schäfer, A., Schmidt, B., Köppel, R., & Hankeln, T. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-639 · Cited by 66
Efficient privacy-preserving string search and an application in genomics
Shimizu, K., Nuida, K., & Rätsch, G. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw050 · Cited by 63
deSALT: fast and accurate long transcriptomic read alignment with de Bruijn graph-based index
Liu, B., Liu, Y., Li, J., Guo, H., Zang, T., & Wang, Y. (2019) · Genome biology · doi:10.1186/s13059-019-1895-9 · Cited by 64
PERFect: PERmutation Filtering test for microbiome data
Smirnova, E., Huzurbazar, S. V., & Jafari, F. (2018) · Biostatistics · doi:10.1093/biostatistics/kxy020 · Cited by 66
FANSe2: A Robust and Cost-Efficient Alignment Tool for Quantitative Next-Generation Sequencing Applications
Xiao, C., Mai, Z., Lian, X., Zhong, J., Jin, J., He, Q., & Zhang, G. (2014) · PLoS ONE · doi:10.1371/journal.pone.0094250 · Cited by 63
Impact of OCR Errors on the Use of Digital Libraries: Towards a Better Access to Information
Chiron, G., Doucet, A., Coustaty, M., Visani, M., & Moreux, J.-P. (2017) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1109/jcdl.2017.7991582 · Cited by 63
Changes in the transcriptome of morula-stage bovine embryos caused by heat shock: relationship to developmental acquisition of thermotolerance
Sakatani, M., Bonilla, L., Dobbs, K. B., Block, J., Ozawa, M., Shanker, S., Yao, J., & Hansen, P. J. (2013) · Reproductive Biology and Endocrinology · doi:10.1186/1477-7827-11-3 · Cited by 62
The effect of next-generation sequencing technology on complex trait research
Day-Williams, A., & Zeggini, E. (2010) · European Journal of Clinical Investigation · doi:10.1111/j.1365-2362.2010.02437.x · Cited by 63
Challenges and opportunities in understanding microbial communities with metagenome assembly (accompanied by IPython Notebook tutorial)
Howe, A., & Chain, P. (2015) · Frontiers in Microbiology · doi:10.3389/fmicb.2015.00678 · Cited by 62
Comparison of Burrows-Wheeler Transform-Based Mapping Algorithms Used in High-Throughput Whole-Genome Sequencing: Application to Illumina Data for Livestock Genomes1
Keel, B. N., & Snelling, W. M. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00035 · Cited by 59
Mapping the non-standardized biases of ribosome profiling
Bartholomäus, A., Campo, C. D., & Ignatova, Z. (2015) · Biological Chemistry · doi:10.1515/hsz-2015-0197 · Cited by 54
Accelerating the Next Generation Long Read Mapping with the FPGA-Based System
Chen, P., Wang, C., Xi, L., & Zhou, X. (2014) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2014.2326876 · Cited by 58
Optimizing multiple sequence alignments using a genetic algorithm based on three objectives: structural information, non-gaps percentage and totally conserved columns
Ortuño, F. M., Valenzuela, O., Rojas, F., Pomares, H., Pérez-Florido, J., Urquiza, J., & Rojas, I. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt360 · Cited by 60
Conserved noncoding sequences provide insights into regulatory sequence and loss of gene expression in maizePreprint
Song, B., Buckler, E. S., Wang, H., Wu, Y., Rees, E. R., Kellogg, E. A., Gates, D. J., Khaipho-Burch, M., Bradbury, P. J., Ross‐Ibarra, J., et al. (2021) · Genome Research · doi:10.1101/gr.266528.120 · Cited by 57
Performance comparison of SNP detection tools with illumina exome sequencing data—an assessment using both family pedigree information and sample-matched SNP array data
Yi, M., Zhao, Y., Li, J., He, M., Kebebew, E., & Stephens, R. M. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku392 · Cited by 61
Lessons from next-generation sequencing analysis in hematological malignancies
Braggio, E., Egan, J. B., Fonseca, R., & Stewart, A. K. (2013) · Blood Cancer Journal · doi:10.1038/bcj.2013.26 · Cited by 57
The missing indels: an estimate of indel variation in a human genome and analysis of factors that impede detection
Jiang, Y., Turinsky, A. L., & Brudno, M. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv677 · Cited by 58
The interplay of host genetic factors and Epstein-Barr virus in the development of nasopharyngeal carcinoma
Lung, M. L., Cheung, A. K. L., Ko, J. M. Y., Lung, H. L., Cheng, Y., & Dai, W. (2014) · 癌症：英文版 · doi:10.5732/cjc.014.10170 · Cited by 55
WEP: a high-performance analysis pipeline for whole-exome data
D’Antonio, M., Meo, P. D. D., Paoletti, D., Elmi, B., Pallocca, M., Sanna, N., Picardi, E., Pesole, G., & Castrignanò, T. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-s7-s11 · Cited by 55
Accelerating Millions of Short Reads Mapping on a Heterogeneous Architecture with FPGA Accelerator
Tang, W., Wang, W., Duan, B., Zhang, C., Tan, G., Zhang, P., & Sun, N. (2012) · IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM) · doi:10.1109/fccm.2012.39 · Cited by 55
Temporal rules discovery for web data cleaning
Abedjan, Z., Akçora, C. G., Ouzzani, M., Papotti, P., & Stonebraker, M. R. (2015) · Proceedings of the VLDB Endowment · doi:10.14778/2856318.2856328 · Cited by 55
WGS Analysis and Interpretation in Clinical and Public Health Microbiology Laboratories: What Are the Requirements and How Do Existing Tools Compare?
Wyres, K. L., Conway, T., Garg, S., Queiroz, C., Reumann, M., Holt, K. E., & Rusu, L. C. (2014) · Pathogens · doi:10.3390/pathogens3020437 · Cited by 54
TSSV: a tool for characterization of complex allelic variants in pure and mixed genomes
Anvar, S. Y., van der Gaag, K. J., van der Heijden, J. W. F., Veltrop, M. H. A. M., Vossen, R. H. A. M., de Leeuw, R. H., Breukel, C., Buermans, H. P. J., Verbeek, J. S., de Knijff, P., et al. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu068 · Cited by 55
Bioinformatics Tools for Genome-Wide Epigenetic Research
Angarica, V. E., & del Sol, A. (2017) · Advances in experimental medicine and biology · doi:10.1007/978-3-319-53889-1_25 · Cited by 56
Flexible and scalable genotyping-by-sequencing strategies for population studies
Heffelfinger, C., Fragoso, C., Moreno, M. A., Overton, J. D., Mottinger, J. P., Zhao, H., Tohme, J. M., & Dellaporta, S. L. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-979 · Cited by 53
Differentially expressed genes from RNA-Seq and functional enrichment results are affected by the choice of single-end versus paired-end reads and stranded versus non-stranded protocols
Corley, S., MacKenzie, K. L., Beverdam, A., Roddam, L. F., & Wilkins, M. R. (2017) · BMC Genomics · doi:10.1186/s12864-017-3797-0 · Cited by 52
Pash 3.0: A versatile software package for read mapping and integrative analysis of genomic and epigenomic variation using massively parallel DNA sequencing
Coarfa, C., Yu, F., Miller, C. A., Chen, Z., Harris, R. A., & Milosavljevic, A. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-572 · Cited by 53
Lithium-responsive genes and gene networks in bipolar disorder patient-derived lymphoblastoid cell lines
Breen, M. S., White, C., Shekhtman, T., Lin, K., Looney, D. J., Woelk, C. H., & Kelsoe, J. R. (2016) · The Pharmacogenomics Journal · doi:10.1038/tpj.2016.50 · Cited by 52
Revolutionizing Biological Science: The Synergy of Genomics in Health, Bioinformatics, Agriculture, and Artificial Intelligence
Biswas, A., Kumari, A., Gaikwad, D. S., & Pandey, D. K. (2023) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2023.0197 · Cited by 56
Massively Parallel Sequencing Approaches for Characterization of Structural Variation
Koboldt, D. C., Larson, D. E., Chen, K., Ding, L., & Wilson, R. K. (2011) · Methods in molecular biology · doi:10.1007/978-1-61779-507-7_18 · Cited by 52
Transcriptome sequencing of Prunus sp. rootstocks roots to identify candidate genes involved in the response to root hypoxia
Arismendi, M. J., Almada, R., Pimentel, P., Bastías, A., Salvatierra, A., Rojas, P., Hinrichsen, P., Pinto, M. E., Genova, A. D., Travisany, D., et al. (2015) · Tree Genetics & Genomes · doi:10.1007/s11295-015-0838-1 · Cited by 51
Complete Genome Sequencing of Influenza A Viruses Using Next-Generation Sequencing
Lee, D. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0346-8_6 · Cited by 50
Optimizing Read Mapping to Reference Genomes to Determine Composition and Species Prevalence in Microbial Communities
Martin, J. C., Sykes, S. M., Young, S., Kota, K., Sanka, R., Sheth, N. U., Orvis, J., Sodergren, E. J., Wang, Z., Weinstock, G. M., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0036427 · Cited by 50
SATB1-mediated functional packaging of chromatin into loops
Kohwi-shigematsu, T., Kohwi, Y., Takahashi, K., Richards, H. W., Ayers, S. D., Han, H., & Cai, S. (2012) · Methods · doi:10.1016/j.ymeth.2012.06.019 · Cited by 51
Accelerating Sequence Alignment to Graphs
Jain, C., Dilthey, A., Misra, S., Zhang, H., & Aluru, S. (2019) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2019.00055 · Cited by 50
Sequence Alignment/Map format: a comprehensive review of approaches and applications
Liu, Y., Shen, X., Gong, Y., Liu, Y., Song, B., & Zeng, X. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad320 · Cited by 49
Characterizing and annotating the genome using RNA-seq data
Chen, G., Shi, T., & Shi, L. M. (2016) · Science China Life Sciences · doi:10.1007/s11427-015-0349-4 · Cited by 53
Application of second-generation sequencing to cancer genomics
Robison, K. E. (2010) · Briefings in Bioinformatics · doi:10.1093/bib/bbq013 · Cited by 48
BioSEAL
Kaplan, R., Yavits, L., & Ginosasr, R. (2020) · ACM International Systems and Storage Conference (SYSTOR) · doi:10.1145/3383669.3398279 · Cited by 48
Clinical Genome Sequencing
Green, R., Rehm, H., & Kohane, I. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-382227-7.00009-4 · Cited by 55
Objective and Comprehensive Evaluation of Bisulfite Short Read Mapping Tools
Tran, H. T., Porter, J., Sun, M.-A., Xie, H., & Zhang, L. (2014) · Advances in Bioinformatics · doi:10.1155/2014/472045 · Cited by 48
Versatile Succinct Representations of the Bidirectional Burrows-Wheeler Transform
Belazzougui, D., Cunial, F., Kärkkäinen, J., & Mäkinen, V. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40450-4_12 · Cited by 47
A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data
Hintzsche, J. D., Robinson, W. A., & Tan, A. C. (2016) · International Journal of Genomics · doi:10.1155/2016/7983236 · Cited by 48
Comparative genomic analysis of two Burkholderia glumae strains from different geographic origins reveals a high degree of plasticity in genome structure associated with genomic islands
Francis, F., Kim, J., Ramaraj, T., Farmer, A. D., Rush, M. C., & Ham, J. H. (2013) · Molecular Genetics and Genomics · doi:10.1007/s00438-013-0744-x · Cited by 46
Differential Expression Analysis of RNA-seq Reads: Overview, Taxonomy, and Tools
Chowdhury, H. A., Bhattacharyya, D. K., & Kalita, J. K. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2873010 · Cited by 49
FANSe: an accurate algorithm for quantitative mapping of large scale sequencing reads
Zhang, G., Fedyunin, I., Kirchner, S., Xiao, C., Valleriani, A., & Ignatova, Z. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks196 · Cited by 45
New approaches to Prunus transcriptome analysis
Martínez-Gómez, P., Crisosto, C. H., Bonghi, C., & Rubio, M. (2011) · Genetica · doi:10.1007/s10709-011-9580-2 · Cited by 45
Kart: a divide-and-conquer algorithm for NGS read alignment
Lin, H.-N., & Hsu, W. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx189 · Cited by 45
A Fast Approximate Algorithm for Mapping Long Reads to Large Reference Databases
Jain, C., Dilthey, A., Koren, S., Aluru, S., & Phillippy, A. M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-56970-3_5 · Cited by 45
BURST enables mathematically optimal short-read alignment for big dataPreprint
Al‐Ghalith, G. A., & Knights, D. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.08.287128 · Cited by 45
Evaluation of Alignment Algorithms for Discovery and Identification of Pathogens Using RNA-Seq
Borozan, I., Watt, S. N., & Ferretti, V. (2013) · PLoS ONE · doi:10.1371/journal.pone.0076935 · Cited by 44
SIMPLEX: Cloud-Enabled Pipeline for the Comprehensive Analysis of Exome Sequencing Data
Fischer, M., Snajder, R., Pabinger, S., Dander, A., Schossig, A. S., Zschocke, J., Trajanoski, Z., & Stocker, G. (2012) · PLoS ONE · doi:10.1371/journal.pone.0041948 · Cited by 44
Big data challenges and opportunities in high-throughput sequencing
Ward, R. M., Schmieder, R., Highnam, G., & Mittelman, D. (2013) · Systems Biomedicine · doi:10.4161/sysb.24470 · Cited by 44
Structural variation discovery in the cancer genome using next generation sequencing: Computational solutions and perspectives
Liu, B., Conroy, J. M., Morrison, C. D., Odunsi, A., Qin, M., Wei, L., Trump, D. L., Johnson, C. S., Liu, S., & Wang, J. (2015) · Oncotarget · doi:10.18632/oncotarget.3491 · Cited by 45
A Genome-Wide Survey of Highly Expressed Non-Coding RNAs and Biological Validation of Selected Candidates in Agrobacterium tumefaciens
Lee, K., Huang, X., Yang, C., Lee, D., Ho, V., Nobuta, K., Fan, J., & Wang, K. (2013) · PLoS ONE · doi:10.1371/journal.pone.0070720 · Cited by 43
How do alignment programs perform on sequencing data with varying qualities and from repetitive regions?
Yu, X., Guda, K., Willis, J. E., Veigl, M. L., Wang, Z., Markowitz, S. D., Adams, M. D., & Sun, S. (2012) · BioData Mining · doi:10.1186/1756-0381-5-6 · Cited by 43
The bench scientist's guide to statistical analysis of RNA-Seq data
Yendrek, C. R., Ainsworth, E. A., & Thimmapuram, J. (2012) · BMC Research Notes · doi:10.1186/1756-0500-5-506 · Cited by 43
Suitability of Different Mapping Algorithms for Genome-Wide Polymorphism Scans with Pool-Seq Data
Kofler, R., Langmüller, A. M., Nouhaud, P., Otte, K. A., & Schlötterer, C. (2016) · G3 Genes Genomes Genetics · doi:10.1534/g3.116.034488 · Cited by 41
merAligner: A Fully Parallel Sequence Aligner
Georganas, E., Buluç, A., Chapman, J., Oliker, L., Rokhsar, D. S., & Yelick, K. (2015) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2015.96 · Cited by 41
HIVE-Hexagon: High-Performance, Parallelized Sequence Alignment for Next-Generation Sequencing Data Analysis
Santana-Quintero, L. V., Dingerdissen, H., Thierry‐Mieg, J., Mazumder, R., & Simonyan, V. V. (2014) · PLoS ONE · doi:10.1371/journal.pone.0099033 · Cited by 40
Molecular homology and multiple-sequence alignment: an analysis of concepts and practice
Morrison, D. A., Morgan, M. J., & Kelchner, S. A. (2015) · Australian Systematic Botany · doi:10.1071/sb15001 · Cited by 41
Optimizing Burrows-Wheeler Transform-Based Sequence Alignment on Multicore Architectures
Zhang, J., Lin, H., Balaji, P., & Feng, W.-C. (2013) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid.2013.67 · Cited by 40
Sequencing technologies and tools for short tandem repeat variation detection
Cao, M. D., Balasubramanian, S., & Mikael Bodén, A. (2014) · Briefings in Bioinformatics · doi:10.1093/bib/bbu001 · Cited by 44
rNA: a fast and accurate short reads numerical aligner
Vezzi, F., Fabbro, C. D., Tomescu, A. I., & Policriti, A. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr617 · Cited by 40

Authors: Li, H., & Homer, N.
Affiliations: Broad Institute (US)
ORCID: Li, H.
Venue: Briefings in Bioinformatics
Publisher: Oxford University Press (OUP)
Published: 2010-05-11
Volume: 11
Issue: 5
Pages: 473-483
ISSN: 1467-5463 (ISSN-L)
1477-4054
DOI: 10.1093/bib/bbq015
PMC: PMC2943993
OpenAlex: W2093931624
Cited by: 875
References: 74
Citations by year: 2026: 11 · 2025: 21 · 2024: 22 · 2023: 21 · 2022: 20 · 2021: 37 · 2020: 41 · 2019: 41 · 2018: 69 · 2017: 52 · 2016: 74 · 2015: 88 · 2014: 118 · 2013: 105 · 2012: 83
FWCI: 7.25
Type: journal article
Language: en
Open access: bronze
doi.org
Locations: Briefings in Bioinformatics (publishedVersion)
Europe PMC (PubMed Central) (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Funders: National Institutes of Health
National Human Genome Research Institute (U01HG005210, U01HG005208)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Physical Sciences › Mathematics › Theoretical Computer Science › Algorithms and Data Compression
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fractal and DNA sequence analysis
Keywords: sequence alignment, next-generation sequencing, short-read alignment, long-read sequencing, alignment algorithms, reference genome mapping, cloud computing, bioinformatics software
MeSH terms: Algorithms, Base Sequence, Humans, Software, Genome, Human, Sequence Alignment, Sequence Analysis, DNA

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

A survey of sequence alignment algorithms for next-generation sequencing

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks