Ben Langmead

129.4k total citations · 9 hit papers
79 papers, 79.0k citations indexed

About

Ben Langmead is a scholar working on Molecular Biology, Artificial Intelligence and Genetics. According to data from OpenAlex, Ben Langmead has authored 79 papers receiving a total of 79.0k indexed citations (citations by other indexed papers that have themselves been cited), including 71 papers in Molecular Biology, 25 papers in Artificial Intelligence and 11 papers in Genetics. Recurrent topics in Ben Langmead's work include Genomics and Phylogenetic Studies (47 papers), Algorithms and Data Compression (23 papers) and Gene expression and cancer classification (17 papers). Ben Langmead is often cited by papers focused on Genomics and Phylogenetic Studies (47 papers), Algorithms and Data Compression (23 papers) and Gene expression and cancer classification (17 papers). Ben Langmead collaborates with scholars based in United States, Canada and Italy. Ben Langmead's co-authors include Steven L. Salzberg, Daehwan Kim, Mihai Pop, Cole Trapnell, Jennifer Lu, Derrick E. Wood, Rafael A. Irizarry, Kasper D. Hansen, Jeffrey T. Leek and Héctor Corrada Bravo and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and Nature Genetics.

In The Last Decade

Ben Langmead

73 papers receiving 78.4k citations

Hit Papers

Fast gapped-read alignment with Bowtie 2 2009 2026 2014 2020 2012 2009 2015 2019 2010 10.0k 20.0k 30.0k

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ben Langmead United States 31 49.5k 18.4k 11.2k 8.7k 8.2k 79 79.0k
Simon Anders Germany 31 51.5k 1.0× 15.8k 0.9× 9.6k 0.9× 11.8k 1.4× 5.6k 0.7× 59 87.9k
Jun Wang China 107 34.0k 0.7× 11.7k 0.6× 9.5k 0.8× 7.9k 0.9× 4.3k 0.5× 1.9k 66.2k
Michael I. Love United States 31 39.1k 0.8× 11.3k 0.6× 7.0k 0.6× 8.9k 1.0× 4.6k 0.6× 93 68.1k
Wolfgang Huber Germany 72 67.0k 1.4× 17.8k 1.0× 12.6k 1.1× 14.6k 1.7× 5.9k 0.7× 249 109.8k
Minoru Kanehisa Japan 75 76.3k 1.5× 14.1k 0.8× 10.1k 0.9× 12.2k 1.4× 11.1k 1.3× 266 114.1k
Gábor Marth United States 34 32.6k 0.7× 13.2k 0.7× 21.3k 1.9× 5.9k 0.7× 6.1k 0.7× 74 60.2k
Robert E. Handsaker United States 23 31.4k 0.6× 12.4k 0.7× 21.2k 1.9× 5.8k 0.7× 5.6k 0.7× 28 59.7k
Sean R. Eddy United States 80 54.3k 1.1× 18.3k 1.0× 9.1k 0.8× 4.2k 0.5× 13.1k 1.6× 127 75.9k
Michael W. Pfaffl Germany 47 30.7k 0.6× 9.5k 0.5× 6.7k 0.6× 6.1k 0.7× 4.3k 0.5× 247 62.0k
Gonçalo R. Abecasis United States 85 41.4k 0.8× 13.2k 0.7× 36.0k 3.2× 7.5k 0.9× 5.8k 0.7× 198 88.1k

Countries citing papers authored by Ben Langmead

Since Specialization
Citations

This map shows the geographic impact of Ben Langmead's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ben Langmead with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ben Langmead more than expected).

Fields of papers citing papers by Ben Langmead

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ben Langmead. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ben Langmead. The network helps show where Ben Langmead may publish in the future.

Co-authorship network of co-authors of Ben Langmead

This figure shows the co-authorship network connecting the top 25 collaborators of Ben Langmead. A scholar is included among the top collaborators of Ben Langmead based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ben Langmead. Ben Langmead is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Hwang, Stephen, et al.. (2025). Mem-based pangenome indexing for k-mer queries. Algorithms for Molecular Biology. 20(1). 3–3. 1 indexed citations
2.
Boucher, Christina, et al.. (2025). Robust 16S rRNA classification based on a compressed LCA index. Genome Research. 35(12). 2650–2660.
3.
Baker, Daniel N. & Ben Langmead. (2023). Genomic sketching with multiplicities and locality-sensitive hashing using Dashing 2. Genome Research. 33(7). gr.277655.123–gr.277655.123. 11 indexed citations
4.
Rossi, Massimiliano, et al.. (2023). Efficient taxa identification using a pangenome index. Genome Research. 33(7). 1069–1077. 5 indexed citations
5.
Chen, Huan, Brian Caffo, Genevieve Stein-O’Brien, et al.. (2022). Two-stage linked component analysis for joint decomposition of multiple biologically related data sets. Biostatistics. 23(4). 1200–1217. 3 indexed citations
6.
Dyjack, Nathan, Daniel N. Baker, Vladimir Braverman, Ben Langmead, & Stephanie C. Hicks. (2022). A scalable and unbiased discordance metric with H +. Biostatistics. 25(1). 188–202. 1 indexed citations
7.
Rossi, Massimiliano, Marco Antônio Oliva, Paola Bonizzoni, et al.. (2022). Finding Maximal Exact Matches Using the r-Index. Journal of Computational Biology. 29(2). 188–194. 1 indexed citations
8.
Rossi, Massimiliano, Marco Antônio Oliva, Ben Langmead, Travis Gagie, & Christina Boucher. (2022). MONI: A Pangenomic Index for Finding Maximal Exact Matches. Journal of Computational Biology. 29(2). 169–187. 27 indexed citations
9.
Chen, Nae-Chyun, et al.. (2021). LevioSAM: fast lift-over of variant-aware reference alignments. Bioinformatics. 37(22). 4243–4245. 8 indexed citations
10.
Wilks, Christopher, et al.. (2021). Megadepth: efficient coverage quantification for BigWigs and BAMs. Bioinformatics. 37(18). 3014–3016. 14 indexed citations
11.
Darby, Charlotte A., et al.. (2020). Vargas: heuristic-free alignment for assessing linear and graph read aligners. Bioinformatics. 36(12). 3712–3718. 15 indexed citations
12.
Kuhnle, Alan, et al.. (2020). Matching Reads to Many Genomes with the r -Index. Journal of Computational Biology. 27(4). 514–518. 5 indexed citations
13.
Wulfridge, Phillip, Ben Langmead, Andrew P. Feinberg, & Kasper D. Hansen. (2019). Analyzing whole genome bisulfite sequencing data from highly divergent genotypes. Nucleic Acids Research. 47(19). e117–e117. 18 indexed citations
14.
Boucher, Christina, et al.. (2019). Prefix-free parsing for building big BWTs. CINECA IRIS Institutial research information system (University of Pisa). 33 indexed citations
15.
Langmead, Ben, et al.. (2018). Scaling read aligners to hundreds of threads on general-purpose processors. Bioinformatics. 35(3). 421–432. 492 indexed citations breakdown →
16.
Wilks, Christopher, et al.. (2017). Snaptron: querying splicing patterns across tens of thousands of RNA-seq samples. Bioinformatics. 34(1). 114–116. 24 indexed citations
17.
Nellore, Abhinav, Christopher Wilks, Kasper D. Hansen, Jeffrey T. Leek, & Ben Langmead. (2016). Rail-dbGaP: analyzing dbGaP-protected data in the cloud with Amazon Elastic MapReduce. Bioinformatics. 32(16). 2551–2553. 3 indexed citations
18.
Nellore, Abhinav, Leonardo Collado‐Torres, Andrew E. Jaffe, et al.. (2016). Rail-RNA: scalable analysis of RNA-seq splicing and coverage. Bioinformatics. 33(24). 4033–4040. 38 indexed citations
19.
Kim, Daehwan, Ben Langmead, & Steven L. Salzberg. (2015). HISAT: a fast spliced aligner with low memory requirements. Nature Methods. 12(4). 357–360. 15557 indexed citations breakdown →
20.
Song, Li, Liliana Florea, & Ben Langmead. (2014). Lighter: fast and memory-efficient sequencing error correction without counting. Genome biology. 15(11). 509–509. 157 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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