Evidence map›Paper›PMID 39637210›Full record

ArticlePloS one2024

SCRMshaw: Supervised cis-regulatory module prediction for insect genomes.

Hasiba Asma, Luna Liu, Marc S Halfon

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Hasiba AsmaDepartments of Biochemistry, University at Buffalo-State University of New York, Buffalo, NY, United States of America.
Luna LiuBiomedical Informatics, University at Buffalo-State University of New York, Buffalo, NY, United States of America.
Marc S HalfonDepartments of Biochemistry, University at Buffalo-State University of New York, Buffalo, NY, United States of America.ORCID 0000-0002-4149-2705

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As the number of sequenced insect genomes continues to grow, there is a pressing need for rapid and accurate annotation of their regulatory component. SCRMshaw is a computational tool designed to predict cis-regulatory modules ("enhancers") in the genomes of various insect species. A key advantage of SCRMshaw is its accessibility. It requires minimal resources-just a genome sequence and training data from known Drosophila regulatory sequences, which are readily available for download. Even users with modest computational skills can run SCRMshaw on a desktop computer for basic applications, although a high-performance computing cluster is recommended for optimal results. SCRMshaw can be tailored to specific needs: users can employ a single set of training data to predict enhancers associated with a particular gene expression pattern, or utilize multiple sets to provide a first-pass regulatory annotation for a newly-sequenced genome. This protocol provides an extensive update to the previously published SCRMshaw protocol and aligns with the methods used in a recent annotation of over 30 insect regulatory genomes. It includes the most recent modifications to the SCRMshaw protocol and details an end-to-end pipeline that begins with a sequenced genome and ends with a fully-annotated regulatory genome. Relevant scripts are available via GitHub, and a living protocol that will be updated as necessary is linked to this article at protocols.io.

Indexed as

Genome, InsectSoftwareAnimalsComputational BiologyEnhancer Elements, Genetic

Identifiers

PMID39637210
PMCPMC11620701

What Socratic 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 Socratic graph.