Evidence map›Paper›PMID 26519503›Full record

ArticleBioinformatics (Oxford, England)2016

MMR: a tool for read multi-mapper resolution.

André Kahles, Jonas Behr, Gunnar Rätsch

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Mobile genomics: tools and techniques for tackling transposons.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2020
    Review
  10. Handling multi-mapped reads in RNA-seq.Computational and structural biotechnology journal · 2020
    Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Gene Expression Pattern and Regulatory Network ofInternational journal of genomics · 2019
    Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

André KahlesMemorial Sloan Kettering Cancer Center, Computational Biology Center, 1275 York Avenue, New York, NY 10065, USA.
Jonas BehrMemorial Sloan Kettering Cancer Center, Computational Biology Center, 1275 York Avenue, New York, NY 10065, USA.
Gunnar RätschMemorial Sloan Kettering Cancer Center, Computational Biology Center, 1275 York Avenue, New York, NY 10065, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

motivationMapping high-throughput sequencing data to a reference genome is an essential step for most analysis pipelines aiming at the computational analysis of genome and transcriptome sequencing data. Breaking ties between equally well mapping locations poses a severe problem not only during the alignment phase but also has significant impact on the results of downstream analyses. We present the multi-mapper resolution (MMR) tool that infers optimal mapping locations from the coverage density of other mapped reads.

resultsFiltering alignments with MMR can significantly improve the performance of downstream analyses like transcript quantitation and differential testing. We illustrate that the accuracy (Spearman correlation) of transcript quantification increases by 15% when using reads of length 51. In addition, MMR decreases the alignment file sizes by more than 50%, and this leads to a reduced running time of the quantification tool. Our efficient implementation of the MMR algorithm is easily applicable as a post-processing step to existing alignment files in BAM format. Its complexity scales linearly with the number of alignments and requires no further inputs. AVAILABILITY AND IMPLEMENTATION: Open source code and documentation are available for download at http://github.com/ratschlab/mmr Comprehensive testing results and further information can be found at http://bioweb.me/mmr. CONTACT: andre.kahles@ratschlab.org or gunnar.ratsch@ratschlab.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

SoftwareAlgorithmsGenomeHigh-Throughput Nucleotide SequencingSequence Alignment

Identifiers

PMID26519503
PMCPMC4795617

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.