Evidence map›Paper›PMID 40459855›Full record

ArticleBioinformatics (Oxford, England)2026

NEFFy: a versatile tool for computing the number of effective sequences.

Maryam Haghani, Debswapna Bhattacharya, T M Murali

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

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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.

Maryam HaghaniDepartment of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States.ORCID 0009-0006-3377-0417
Debswapna BhattacharyaDepartment of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States.ORCID 0000-0002-9630-0141
T M MuraliDepartment of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States.ORCID 0000-0003-3688-4672

Funding

GPU-accelerated high-performance computing to supercharge foundational deep learning method development for scalable and accurate prediction of protein structuresR35GM138146 · NIGMS · VIRGINIA POLYTECHNIC INST AND ST UNIV · PI Debswapna Bhattacharya · 2020 to 2026
$2.5M
National Science Foundation 2200045National Science Foundation DBI 2208679NIGMS NIH HHS R35 GM138146NIGMS NIH HHS R35GM138146
6 · The paper itself

Abstract

motivationA Multiple Sequence Alignment (MSA) contains fundamental evolutionary information that is useful in the prediction of structure and function of proteins and nucleic acids. The "Number of Effective Sequences" (NEFF) quantifies the diversity of sequences of an MSA. While several tools embed NEFF calculation with various options, none are standalone tools for this purpose, and they do not offer all the available options.

resultsWe developed NEFFy, the first software package to integrate all these options and calculate NEFF across diverse MSA formats for proteins, RNAs, and DNAs. It surpasses existing tools in functionality without compromising computational efficiency and scalability. NEFFy also offers per-residue NEFF calculation and supports NEFF computation for MSAs of multimeric proteins, with the capability to be extended to DNAs and RNAs. AVAILABILITY AND IMPLEMENTATION: NEFFy is released as open-source software under the GNU Public License v3.0. The source code in C++ and a Python wrapper are available at https://github.com/Maryam-Haghani/NEFFy. To ensure users can fully leverage these capabilities, comprehensive documentation and examples are provided at https://Maryam-Haghani.github.io/NEFFy.

Indexed as

Computational BiologySequence AlignmentSoftwareDNAProteinsRNASequence Analysis, DNASequence Analysis, ProteinDNAProteinsRNA

Identifiers

PMID40459855
PMCPMC13242184

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.