The GEM mapper: fast, accurate and versatile alignment by filtration

By Santiago Marco-Sola, M. Sammeth, Roderic Guigó, Paolo Ribeca. Nature Methods, 2012

578 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/71150718ec7affbc4f9130f55f925af0dd956651

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Gordon K. Smyth
2. Heng Li
3. Wei Shi
4. Yang Liao
5. Ben Langmead
6. Aviv Regev
7. C. Wilks
8. Valentin Antonescu

In Molecular Biology

1. Wei Shi
2. Gordon K. Smyth
3. Yang Liao
4. Heng Li
5. Laura L. Elo
6. Ben Langmead
7. Aviv Regev
8. Ana Conesa

In Plant Science

1. Alexander Dobin
2. Aviv Regev
3. Ali Mortazavi
4. Brian J. Haas
5. Wei Shi
6. Gordon K. Smyth
7. Heng Li
8. Nathalie Pochet

Citations in the corpus

• A survey of best practices for RNA-seq data analysis (2016, background): “Several other mappers exist that are optimized to identify SNPs or indels (GSNAP [202], PALMapper [203] MapSplice [204]), detect non-canonical splice junctions (STAR [15], MapSplice [204]), achieve ultra-fast mapping (GEM [205]) or map long-reads (STAR [15]).”
• Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM (2013, methodology): “GEM is configured to allow up to 5 gaps and to output suboptimal alignments (option ‘–e5 –m5 –s1’ for SE and ‘– e5 –m5 –s1 –pb’ for PE).”
• Personalized Nutrition by Prediction of Glycemic Responses. (2015, methodology): “We assigned length-normalized RA of genes, obtained by similar mapping with GEM to the reference catalog of (Li et al., 2014), to KEGG Orthology (KO) entries (Kanehisa and Goto, 2000), and these were then normalized to a sum of 1.”
• Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods (2019, methodology): “…were more readily detected with longer vs. shorter reads, and de novo ChimPipe [21] R The GEMtools RNA-seq pipeline [22] and GEM alignment utility [23] are used to capture discordant and chimeric read alignments, and fusion candidates are filtered according to fusion evidence and additional…”
• Scaling read aligners to hundreds of threads on general-purpose processors (2017, background): “It can also exceed available memory for aligners with larger data structures, such as GEM [20] and SNAP [21], as shown previously [6].”
• The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote (2013, background): “Most aligners then work out from the location that the seed mapped to, trying to match the remainder of the read to the genome surrounding the original location, a process often called the extension step (2).”
• featureCounts: an efficient general purpose program for assigning sequence reads to genomic features (2013, methodology): “Many statistical analysis methods have been developed to detect differential expression or differential binding on the basis of read counts (Anders and Huber, 2010; Auer and Doerge, 2011; Hardcastle and Kelly, 2010; Li et al., 2012; McCarthy et al., 2012; Wu et al., 2013).”
• Comparative performance of the BGISEQ-500 vs Illumina HiSeq2500 sequencing platforms for palaeogenomic sequencing (2017, methodology): “These were then mapped back onto the assembly using GEM (GEM, RRID:SCR 005339) [102] with a maximum divergence set to 95% and retaining all possible mappings.”