MerCat2: a Versatile -mer Counter and Diversity Estimator for Database-independent Property Analysis Obtained from Omics Data

Overview

Journal Bioinform Adv

Publisher Oxford University Press

Specialty Biology

Date 2024 May 15

PMID 38745763

Authors

Jose L Figueroa 3rd

Andrew Redinbo

Ajay Panyala

Sean Colby

Maren L Friesen

Lisa Tiemann

Richard Allen White 3rd

Affiliations

Soon will be listed here.

Abstract

Motivation: MerCat2 ("Mer-Catenate2") is a versatile, parallel, scalable and modular property software package for robustly analyzing features in omics data. Using massively parallel sequencing raw reads, assembled contigs, and protein sequences from any platform as input, MerCat2 performs -mer counting of any length , resulting in feature abundance counts tables, quality control reports, protein feature metrics, and graphical representation (i.e. principal component analysis (PCA)).

Results: MerCat2 allows for direct analysis of data properties in a database-independent manner that initializes all data, which other profilers and assembly-based methods cannot perform. MerCat2 represents an integrated tool to illuminate omics data within a sample for rapid cross-examination and comparisons.

Availability And Implementation: MerCat2 is written in Python and distributed under a BSD-3 license. The source code of MerCat2 is freely available at https://github.com/raw-lab/mercat2. MerCat2 is compatible with Python 3 on Mac OS X and Linux. MerCat2 can also be easily installed using bioconda: mamba create -n mercat2 -c conda-forge -c bioconda mercat2.

Citing Articles

Integrating Genomic Data with the Development of CRISPR-Based Point-of-Care-Testing for Bacterial Infections.

Wanitchanon T, Chewapreecha C, Uttamapinant C Curr Clin Microbiol Rep. 2024; 11(4):241-258.

PMID: 39525369 PMC: 11541280. DOI: 10.1007/s40588-024-00236-7.

Utilizing genomic signatures to gain insights into the dynamics of SARS-CoV-2 through Machine and Deep Learning techniques.

Elsherbini A, Elkholy A, Fadel Y, Goussarov G, Elshal A, El-Hadidi M BMC Bioinformatics. 2024; 25(1):131.

PMID: 38539073 PMC: 10967124. DOI: 10.1186/s12859-024-05648-2.

MetaCerberus: distributed highly parallelized HMM-based processing for robust functional annotation across the tree of life.

Figueroa Iii J, Dhungel E, Bellanger M, Brouwer C, White Iii R Bioinformatics. 2024; 40(3).

PMID: 38426351 PMC: 10955254. DOI: 10.1093/bioinformatics/btae119.

References

White 3rd R, Soles S, Gavelis G, Gosselin E, Slater G, Lim D . The Complete Genome and Physiological Analysis of the Eurythermal Firmicute Strain RW2 Isolated From a Freshwater Microbialite, Widely Adaptable to Broad Thermal, pH, and Salinity Ranges. Front Microbiol. 2019; 9:3189. PMC: 6331483. DOI: 10.3389/fmicb.2018.03189. View

Van der Jeugt F, Dawyndt P, Mesuere B . FragGeneScanRs: faster gene prediction for short reads. BMC Bioinformatics. 2022; 23(1):198. PMC: 9148508. DOI: 10.1186/s12859-022-04736-5. View

Wu Y, Simmons B, Singer S . MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics. 2015; 32(4):605-7. DOI: 10.1093/bioinformatics/btv638. View

Jiang B, Song K, Ren J, Deng M, Sun F, Zhang X . Comparison of metagenomic samples using sequence signatures. BMC Genomics. 2012; 13:730. PMC: 3549735. DOI: 10.1186/1471-2164-13-730. View

Howe A, Jansson J, Malfatti S, Tringe S, Tiedje J, Brown C . Tackling soil diversity with the assembly of large, complex metagenomes. Proc Natl Acad Sci U S A. 2014; 111(13):4904-9. PMC: 3977251. DOI: 10.1073/pnas.1402564111. View

Shaffer M, Borton M, McGivern B, Zayed A, La Rosa S, Solden L . DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res. 2020; 48(16):8883-8900. PMC: 7498326. DOI: 10.1093/nar/gkaa621. View

Chen S, Zhou Y, Chen Y, Gu J . fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018; 34(17):i884-i890. PMC: 6129281. DOI: 10.1093/bioinformatics/bty560. View

White 3rd R, Gavelis G, Soles S, Gosselin E, Slater G, Lim D . The Complete Genome and Physiological Analysis of the Microbialite-Dwelling sp. nov; Reveals Genetic Promiscuity and Predicted Adaptations to Environmental Stress. Front Microbiol. 2018; 9:2180. PMC: 6196244. DOI: 10.3389/fmicb.2018.02180. View

Benoit G, Mariadassou M, Robin S, Schbath S, Peterlongo P, Lemaitre C . SimkaMin: fast and resource frugal de novo comparative metagenomics. Bioinformatics. 2019; 36(4):1275-1276. DOI: 10.1093/bioinformatics/btz685. View

10.

Truong D, Franzosa E, Tickle T, Scholz M, Weingart G, Pasolli E . MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat Methods. 2015; 12(10):902-3. DOI: 10.1038/nmeth.3589. View

11.

Hyatt D, LoCascio P, Hauser L, Uberbacher E . Gene and translation initiation site prediction in metagenomic sequences. Bioinformatics. 2012; 28(17):2223-30. DOI: 10.1093/bioinformatics/bts429. View

12.

Wood D, Lu J, Langmead B . Improved metagenomic analysis with Kraken 2. Genome Biol. 2019; 20(1):257. PMC: 6883579. DOI: 10.1186/s13059-019-1891-0. View

13.

Figueroa Iii J, Dhungel E, Bellanger M, Brouwer C, White Iii R . MetaCerberus: distributed highly parallelized HMM-based processing for robust functional annotation across the tree of life. Bioinformatics. 2024; 40(3). PMC: 10955254. DOI: 10.1093/bioinformatics/btae119. View

14.

White 3rd R, Grassa C, Suttle C . First draft genome sequence from a member of the genus agrococcus, isolated from modern microbialites. Genome Announc. 2013; 1(4). PMC: 3695436. DOI: 10.1128/genomeA.00391-13. View

15.

Kokot M, Dlugosz M, Deorowicz S . KMC 3: counting and manipulating k-mer statistics. Bioinformatics. 2017; 33(17):2759-2761. DOI: 10.1093/bioinformatics/btx304. View

16.

Zhang Q, Pell J, Canino-Koning R, Howe A, Brown C . These are not the k-mers you are looking for: efficient online k-mer counting using a probabilistic data structure. PLoS One. 2014; 9(7):e101271. PMC: 4111482. DOI: 10.1371/journal.pone.0101271. View

17.

Lindgreen S, Adair K, Gardner P . An evaluation of the accuracy and speed of metagenome analysis tools. Sci Rep. 2016; 6:19233. PMC: 4726098. DOI: 10.1038/srep19233. View

18.

Li D, Liu C, Luo R, Sadakane K, Lam T . MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015; 31(10):1674-6. DOI: 10.1093/bioinformatics/btv033. View

19.

Parks D, Chuvochina M, Rinke C, Mussig A, Chaumeil P, Hugenholtz P . GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Res. 2021; 50(D1):D785-D794. PMC: 8728215. DOI: 10.1093/nar/gkab776. View

20.

Campbell M, Grice K, Visscher P, Morris T, Wong H, White 3rd R . Functional Gene Expression in Shark Bay Hypersaline Microbial Mats: Adaptive Responses. Front Microbiol. 2020; 11:560336. PMC: 7702295. DOI: 10.3389/fmicb.2020.560336. View