Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 607

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

Using the Basic Local Alignment Search Tool (BLAST)

Mount, David W.·2007·Cold Spring Harbor Protocols

Abstract

INTRODUCTION: The BLAST algorithm was developed as a way to perform DNA and protein sequence similarity searches by an algorithm that is faster than FASTA but considered to be equally as sensitive. Both of these methods follow a heuristic (tried-and-true) method that almost always works to find related sequences in a database search, but does not have the underlying guarantee of an optimal solution like the dynamic programming algorithm. FASTA finds short common patterns in query and database sequences and joins these into an alignment. BLAST is similar to FASTA, but gains a further increase in speed by searching only for rarer, more significant patterns in nucleic acid and protein sequences. BLAST is very popular due to its availability on the World Wide Web through a large server at the National Center for Biotechnology Information (NCBI) and at many other sites. The BLAST algorithm has evolved to provide molecular biologists with a set of very powerful search tools that are freely available to run on many computer platforms. This article is intended to be a “user’s guide” to the principles underlying BLAST.

Overview

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1101/pdb.top17

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

Verification

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.

A few free checks a day · papers already verified open instantly · results are public

Self-check

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

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

Citations

Cites

Show all 8 references

Cited by

See all 607

Metadata

Authors
Mount, D. W.
Venue
Cold Spring Harbor Protocols
Published
2007-07
Volume
2007
Issue
7
Pages
pdb.top17
ISSN
  • 1559-6095 (ISSN-L)
  • 1940-3402
OpenAlex
W1978204300
Cited by
607
References
8
Type
posted content
Language
en
Open access
closed

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

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Using the Basic Local Alignment Search Tool (BLAST). https://app.cochat.ai/papers/10.1101/pdb.top17
The paper
Mount, D. W. (2007). Using the Basic Local Alignment Search Tool (BLAST). Cold Spring Harbor Protocols. https://doi.org/10.1101/pdb.top17

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.