Evidence map›Paper›PMID 17156431›Full record

ArticleBMC biology2006

Composition-based statistics and translated nucleotide searches: improving the TBLASTN module of BLAST.

E Michael Gertz, Yi-Kuo Yu, Richa Agarwala, Alejandro A Schäffer, Stephen F Altschul

Abstract read
In one paragraph

Article in BMC biology, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 366 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
366citing papers in PubMed, 1 pooled it
–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

366 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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306 more citing papers are in PubMed but not listed here.

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

5 authors.

E Michael GertzNational Center for Biotechnology Information, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA. gertz@ncbi.nlm.nih.gov
Yi-Kuo Yu
Richa Agarwala
Alejandro A Schäffer
Stephen F Altschul

Funding

Improvements and Extensions to the BLAST AlgorithmsZIALM000072 · NLM · NATIONAL LIBRARY OF MEDICINE · PI ALTSCHUL, STEPHEN F · 2009 to 2019
$542k
IMPROVEMENTS TO THE BLAST ALGORITHMZ01LM000072 · NLM · NATIONAL LIBRARY OF MEDICINE · PI ALTSCHUL, STEPHEN F · 1996 to 2008
$496k
NCBI Production Resource DevelopmentZ01LM000160 · NLM · NATIONAL LIBRARY OF MEDICINE · PI OSTELL, JAMES M · 2002 to 2006
–
Intramural NIH HHS
6 · The paper itself

Abstract

backgroundTBLASTN is a mode of operation for BLAST that aligns protein sequences to a nucleotide database translated in all six frames. We present the first description of the modern implementation of TBLASTN, focusing on new techniques that were used to implement composition-based statistics for translated nucleotide searches. Composition-based statistics use the composition of the sequences being aligned to generate more accurate E-values, which allows for a more accurate distinction between true and false matches. Until recently, composition-based statistics were available only for protein-protein searches. They are now available as a command line option for recent versions of TBLASTN and as an option for TBLASTN on the NCBI BLAST web server.

resultsWe evaluate the statistical and retrieval accuracy of the E-values reported by a baseline version of TBLASTN and by two variants that use different types of composition-based statistics. To test the statistical accuracy of TBLASTN, we ran 1000 searches using scrambled proteins from the mouse genome and a database of human chromosomes. To test retrieval accuracy, we modernize and adapt to translated searches a test set previously used to evaluate the retrieval accuracy of protein-protein searches. We show that composition-based statistics greatly improve the statistical accuracy of TBLASTN, at a small cost to the retrieval accuracy.

conclusionTBLASTN is widely used, as it is common to wish to compare proteins to chromosomes or to libraries of mRNAs. Composition-based statistics improve the statistical accuracy, and therefore the reliability, of TBLASTN results. The algorithms used by TBLASTN are not widely known, and some of the most important are reported here. The data used to test TBLASTN are available for download and may be useful in other studies of translated search algorithms.

Indexed as

AlgorithmsAnimalsComputational BiologyDatabases, Nucleic AcidDatabases, ProteinHumansProtein BiosynthesisSequence AlignmentSoftware

Identifiers

PMID17156431
PMCPMC1779365

What OpenQuestion holds

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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.