Evidence map›Paper›PMID 38212571›Full record

ArticleBiochemical genetics2024

RFGR: Repeat Finder for Complete and Assembled Whole Genomes and NGS Reads.

Rashmi Sukumaran, K Shahina, Achuthsankar S Nair

Abstract read
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In one paragraph

Article in Biochemical genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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0 citing papers in PubMed, 0 citations in OpenAlex.

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4 · The record

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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 at 1 institution in 1 country.

Rashmi SukumaranDepartment of Computational Biology and Bioinformatics, University of Kerala, Karyavattom, Trivandrum, Kerala, India. rashmi.dcb@keralauniversity.ac.in.
K ShahinaDepartment of Computational Biology and Bioinformatics, University of Kerala, Karyavattom, Trivandrum, Kerala, India.
Achuthsankar S NairDepartment of Computational Biology and Bioinformatics, University of Kerala, Karyavattom, Trivandrum, Kerala, India.
University of Kerala · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Repetitive DNA sequences cause genomic instability and are important genetic markers. Identification of repeats is a critical step in genome annotation and analysis. On the other hand, repeats also pose a technical challenge for genome assembly and alignment programs using NGS data. RFGR is a comprehensive tool that can find exact repetitive sequences in complete genomes and assembled genomes, as well as NGS reads of prokaryotes. For complete genomes, RFGR uses a suffix trees to find seed repeats of repetitive sequences of fixed length with indels. For assembled genomes, RFGR uses a modified Bowtie aligner to find seed repeats of exact repetitive sequences in the contigs/ scaffolds, which are then extended to maximal repeats. The repeats are classified and for repeats near a gene, RFGR reports the gene as well. For the control dataset of E. coli UTI89 and E. coli K12, RFGR reports 35,141 and 49,352 repeats, respectively. For NGS reads, RFGR uses the frequency of the repetitive k-mers to determine FASTQ reads containing repetitive sequences and removes them from the dataset. An E. coli K12 NGS dataset pre-processed using RFGR, on comparison with the original dataset, gives an improved assembly. The N50 value improves by 22.86% with a decrease in size of the assembly graph by nearly 50%. Thus, with RFGR, we achieve a better assembly with reduced computation. RFGR can be improved in terms of the length of the minimum repeat found, extending to find approximate repeats and to be applicable to Eukaryotes as well.

Indexed as

High-Throughput Nucleotide SequencingRepetitive Sequences, Nucleic AcidEscherichia coli K12Genome, BacterialSequence Analysis, DNASoftwareProkaryotic regulatory switchesRepeat identificationSuffix trees

Identifiers

PMID38212571
OpenAlexW4390795997

What OpenQuestion holds

Textmetadata
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Registered trials

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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.