FASTA Files Research Articles

HMSS2: An advanced tool for the analysis of sulphur metabolism, including organosulphur compound transformation, in genome and metagenome assemblies.

The global sulphur cycle has implications for human health, climate change, biogeochemistry and bioremediation. The organosulphur compounds that participate in this cycle not only represent a vast reservoir of sulphur but are also used by prokaryotes as sources of energy and/or carbon. Closely linked to the inorganic sulphur cycle, it involves the interaction of prokaryotes, eukaryotes and chemical processes. However, ecological and evolutionary studies of the conversion of organic sulphur compounds are hampered by the poor conservation of the relevant pathways and their variation even within strains of the same species. In addition, several proteins involved in the conversion of sulphonated compounds are related to proteins involved in sulphur dissimilation or turnover of other compounds. Therefore, the enzymes involved in the metabolism of organic sulphur compounds are usually not correctly annotated in public databases. To address this challenge, we have developed HMSS2, a profiled Hidden Markov Model-based tool for rapid annotation and synteny analysis of organic and inorganic sulphur cycle proteins in prokaryotic genomes. Compared to its previous version (HMS-S-S), HMSS2 includes several new features. HMM-based annotation is now supported by nonhomology criteria and covers the metabolic pathways of important organosulphur compounds, including dimethylsulphoniopropionate, taurine, isethionate, and sulphoquinovose. In addition, the calculation speed has been increased by a factor of four and the available output formats have been extended to include iTol compatible data sets, and customized sequence FASTA files.

MitoHiFi: a python pipeline for mitochondrial genome assembly from PacBio high fidelity reads.

PacBio high fidelity (HiFi) sequencing reads are both long (15-20kb) and highly accurate (> Q20). Because of these properties, they have revolutionised genome assembly leading to more accurate and contiguous genomes. In eukaryotes the mitochondrial genome is sequenced alongside the nuclear genome often at very high coverage. A dedicated tool for mitochondrial genome assembly using HiFi reads is still missing. MitoHiFi was developed within the Darwin Tree of Life Project to assemble mitochondrial genomes from the HiFi reads generated for target species. The input for MitoHiFi is either the raw reads or the assembled contigs, and the tool outputs a mitochondrial genome sequence fasta file along with annotation of protein and RNA genes. Variants arising from heteroplasmy are assembled independently, and nuclear insertions of mitochondrial sequences are identified and not used in organellar genome assembly. MitoHiFi has been used to assemble 374 mitochondrial genomes (368 Metazoa and 6 Fungi species) for the Darwin Tree of Life Project, the Vertebrate Genomes Project and the Aquatic Symbiosis Genome Project. Inspection of 60 mitochondrial genomes assembled with MitoHiFi for species that already have reference sequences in public databases showed the widespread presence of previously unreported repeats. MitoHiFi is able to assemble mitochondrial genomes from a wide phylogenetic range of taxa from Pacbio HiFi data. MitoHiFi is written in python and is freely available on GitHub ( https://github.com/marcelauliano/MitoHiFi ). MitoHiFi is available with its dependencies as a Docker container on GitHub (ghcr.io/marcelauliano/mitohifi:master).

GOgetter: A pipeline for summarizing and visualizing GO slim annotations for plant genetic data.

The functional annotation of genes is a crucial component of genomic analyses. A common way to summarize functional annotations is with hierarchical gene ontologies, such as the Gene Ontology (GO) Resource. GO includes information about the cellular location, molecular function(s), and products/processes that genes produce or are involved in. For a set of genes, summarizing GO annotations using pre-defined, higher-order terms (GO slims) is often desirable in order to characterize the overall function of the data set, and it is impractical to do this manually. The GOgetter pipeline consists of bash and Python scripts. From an input FASTA file of nucleotide gene sequences, it outputs text and image files that list (1) the best hit for each input gene in a set of reference gene models, (2) all GO terms and annotations associated with those hits, and (3) a summary and visualization of GO slim categories for the data set. These output files can be queried further and analyzed statistically, depending on the downstream need(s). GO annotations are a widely used "universal language" for describing gene functions and products. GOgetter is a fast and easy-to-implement pipeline for obtaining, summarizing, and visualizing GO slim categories associated with a set of genes.

HaploCart: Human mtDNA haplogroup classification using a pangenomic reference graph.

Current mitochondrial DNA (mtDNA) haplogroup classification tools map reads to a single reference genome and perform inference based on the detected mutations to this reference. This approach biases haplogroup assignments towards the reference and prohibits accurate calculations of the uncertainty in assignment. We present HaploCart, a probabilistic mtDNA haplogroup classifier which uses a pangenomic reference graph framework together with principles of Bayesian inference. We demonstrate that our approach significantly outperforms available tools by being more robust to lower coverage or incomplete consensus sequences and producing phylogenetically-aware confidence scores that are unbiased towards any haplogroup. HaploCart is available both as a command-line tool and through a user-friendly web interface. The C++ program accepts as input consensus FASTA, FASTQ, or GAM files, and outputs a text file with the haplogroup assignments of the samples along with the level of confidence in the assignments. Our work considerably reduces the amount of data required to obtain a confident mitochondrial haplogroup assignment.

Cyanoseq: A database of cyanobacterial 16S rRNA gene sequences with curated taxonomy.

Cyanobacteria are photosynthetic bacteria that occupy various habitats across the globe, playing critical roles in many of Earth's biogeochemical cycles both in both aquatic and terrestrial systems. Despite their well-known significance, their taxonomy remains problematic and is the subject of much research. Taxonomic issues of Cyanobacteria have consequently led to inaccurate curation within known reference databases, ultimately leading to problematic taxonomic assignment during diversity studies. Recent advances in sequencing technologies have increased our ability to characterize and understand microbial communities, leading to the generation of thousands of sequences that require taxonomic assignment. We herein propose CyanoSeq (https://zenodo.org/record/7569105), a database of cyanobacterial 16S rRNA gene sequences with curated taxonomy. The taxonomy of CyanoSeq is based on the current state of cyanobacterial taxonomy, with ranks from the domain to genus level. Files are provided for use with common naive Bayes taxonomic classifiers, such as those included in DADA2 or the QIIME2 platform. Additionally, FASTA files are provided for creation of de novo phylogenetic trees with (near) full-length 16S rRNA gene sequences to determine the phylogenetic relationship of cyanobacterial strains and/or ASV/OTUs. The database currently consists of 5410 cyanobacterial 16S rRNA gene sequences along with 123 Chloroplast, Bacterial, and Vampirovibrionia (formally Melainabacteria) sequences.

RabbitFX: Efficient Framework for FASTA/Q File Parsing on Modern Multi-Core Platforms.

The continuous growth of generated sequencing data leads to the development of a variety of associated bioinformatics tools. However, many of them are not able to fully exploit the resources of modern multi-core systems since they are bottlenecked by parsing files leading to slow execution times. This motivates the design of an efficient method for parsing sequencing data that can exploit the power of modern hardware, especially for modern CPUs with fast storage devices. We have developed RabbitFX, a fast, efficient, and easy-to-use framework for processing biological sequencing data on modern multi-core platforms. It can efficiently read FASTA and FASTQ files by combining a lightweight parsing method by means of an optimized formatting implementation. Furthermore, we provide user-friendly and modularized C++ APIs that can be easily integrated into applications in order to increase their file parsing speed. As proof-of-concept, we have integrated RabbitFX into three I/O-intensive applications: fastp, Ktrim, and Mash. Our evaluation shows that the inclusion of RabbitFX leads to speedups of at least 11.6 (6.6), 2.4 (2.4), and 3.7 (3.2) compared to the original versions on plain (gzip-compressed) files, respectively. These case studies demonstrate that RabbitFX can be easily integrated into a variety of NGS analysis tools to significantly reduce associated runtimes. It is open source software available at https://github.com/RabbitBio/RabbitFX.

PepTraq: a toolbox for in silico data mining and fast sequence filtering.

The development of de novo sequencing tools has led to the massive production of genomes and transcriptomes from many unconventional animal models. To tackle this huge flow of data, PepTraq brings together many functionalities generally scattered in multiple tools, so that sequences can be filtered on the basis of multiple criteria. It is particularly suitable for the identification of non-annotated transcripts, re-annotation, extraction of secretomes, neuropeptidomes, targeted search for peptides and proteins, preparing specific proteomics/peptidomics fasta files for mass spectrometry (MS) applications, MS data processing, etc. PepTraq is developed in Java, and is available as a desktop application that can be downloaded from https://peptraq.greyc.fr . It is also available as a web application at the same URL for processing small files (10-20MB). The source code is open under a CeCILL-B licence.

POSMM: an efficient alignment-free metagenomic profiler that complements alignment-based profiling

We present here POSMM (pronounced ‘Possum’), Python-Optimized Standard Markov Model classifier, which is a new incarnation of the Markov model approach to metagenomic sequence analysis. Built on the top of a rapid Markov model based classification algorithm SMM, POSMM reintroduces high sensitivity associated with alignment-free taxonomic classifiers to probe whole genome or metagenome datasets of increasingly prohibitive sizes. Logistic regression models generated and optimized using the Python sklearn library, transform Markov model probabilities to scores suitable for thresholding. Featuring a dynamic database-free approach, models are generated directly from genome fasta files per run, making POSMM a valuable accompaniment to many other programs. By combining POSMM with ultrafast classifiers such as Kraken2, their complementary strengths can be leveraged to produce higher overall accuracy in metagenomic sequence classification than by either as a standalone classifier. POSMM is a user-friendly and highly adaptable tool designed for broad use by the metagenome scientific community.

Global landscape of SARS-CoV-2 mutations and conserved regions

BackgroundAt the end of December 2019, a novel strain of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) disease (COVID-19) has been identified in Wuhan, a central city in China, and then spread to every corner of the globe. As of October 8, 2022, the total number of COVID-19 cases had reached over 621 million worldwide, with more than 6.56 million confirmed deaths. Since SARS-CoV-2 genome sequences change due to mutation and recombination, it is pivotal to surveil emerging variants and monitor changes for improving pandemic management.Methods10,287,271 SARS-CoV-2 genome sequence samples were downloaded in FASTA format from the GISAID databases from February 24, 2020, to April 2022. Python programming language (version 3.8.0) software was utilized to process FASTA files to identify variants and sequence conservation. The NCBI RefSeq SARS-CoV-2 genome (accession no. NC_045512.2) was considered as the reference sequence.ResultsSix mutations had more than 50% frequency in global SARS-CoV-2. These mutations include the P323L (99.3%) in NSP12, D614G (97.6) in S, the T492I (70.4) in NSP4, R203M (62.8%) in N, T60A (61.4%) in Orf9b, and P1228L (50.0%) in NSP3. In the SARS-CoV-2 genome, no mutation was observed in more than 90% of nsp11, nsp7, nsp10, nsp9, nsp8, and nsp16 regions. On the other hand, N, nsp3, S, nsp4, nsp12, and M had the maximum rate of mutations. In the S protein, the highest mutation frequency was observed in aa 508–635(0.77%) and aa 381–508 (0.43%). The highest frequency of mutation was observed in aa 66–88 (2.19%), aa 7–14, and aa 164–246 (2.92%) in M, E, and N proteins, respectively.ConclusionTherefore, monitoring SARS-CoV-2 proteomic changes and detecting hot spots mutations and conserved regions could be applied to improve the SARS‐CoV‐2 diagnostic efficiency and design safe and effective vaccines against emerging variants.

(Fasta files) Low frequency of Plasmodium falciparum hrp2/3 deletions from symptomatic infections at a primary healthcare facility in Kilifi, Kenya

(Fasta files) Low frequency of Plasmodium falciparum hrp2/3 deletions from symptomatic infections at a primary healthcare facility in Kilifi, Kenya

In silico analyses of diversity and dissemination of antimicrobial resistance genes and mobile genetics elements, for plasmids of enteric pathogens.

The antimicrobial resistance (AMR) mobilome plays a key role in the dissemination of resistance genes encoded by mobile genetics elements (MGEs) including plasmids, transposons (Tns), and insertion sequences (ISs). These MGEs contribute to the dissemination of multidrug resistance (MDR) in enteric bacterial pathogens which have been considered as a global public health risk. To further understand the diversity and distribution of AMR genes and MGEs across different plasmid types, we utilized multiple sequence-based computational approaches to evaluate AMR-associated plasmid genetics. A collection of 1,309 complete plasmid sequences from Gammaproteobacterial species, including 100 plasmids from each of the following 14 incompatibility (Inc) types: A/C, BO, FIA, FIB, FIC, FIIA, HI1, HI2, I1, K, M, N, P except W, where only 9 sequences were available, was extracted from the National Center for Biotechnology Information (NCBI) GenBank database using BLAST tools. The extracted FASTA files were analyzed using the AMRFinderPlus web-based tools to detect antimicrobial, disinfectant, biocide, and heavy metal resistance genes and ISFinder to identify IS/Tn MGEs within the plasmid sequences. In silico prediction based on plasmid replicon types showed that the resistance genes were diverse among plasmids, yet multiple genes were widely distributed across the plasmids from enteric bacterial species. These findings provide insights into the diversity of resistance genes and that MGEs mediate potential transmission of these genes across multiple plasmid replicon types. This notion was supported by the observation that many IS/Tn MGEs and resistance genes known to be associated with them were common across multiple different plasmid types. Our results provide critical insights about how the diverse population of resistance genes that are carried by the different plasmid types can allow for the dissemination of AMR across enteric bacteria. The results also highlight the value of computational-based approaches and in silico analyses for the assessment of AMR and MGEs, which are important elements of molecular epidemiology and public health outcomes.

BigSeqKit: a parallel Big Data toolkit to process FASTA and FASTQ files at scale.

High-throughput sequencing technologies have led to an unprecedented explosion in the amounts of sequencing data available, which are typically stored using FASTA and FASTQ files. We can find in the literature several tools to process and manipulate those type of files with the aim of transforming sequence data into biological knowledge. However, none of them are well fitted for processing efficiently very large files, likely in the order of terabytes in the following years, since they are based on sequential processing. Only some routines of the well-known seqkit tool are partly parallelized. In any case, its scalability is limited to use few threads on a single computing node. Our approach, BigSeqKit, takes advantage of a high-performance computing-Big Data framework to parallelize and optimize the commands included in seqkit with the aim of speeding up the manipulation of FASTA/FASTQ files. In this way, in most cases, it is from tens to hundreds of times faster than several state-of-the-art tools. At the same time, our toolkit is easy to use and install on any kind of hardware platform (local server or cluster), and its routines can be used as a bioinformatics library or from the command line. BigSeqKit is a very complete and ultra-fast toolkit to process and manipulate large FASTA and FASTQ files. It is publicly available at https://github.com/citiususc/BigSeqKit.

Accurate and fast clade assignment via deep learning and frequency chaos game representation.

Since the beginning of the coronavirus disease 2019 pandemic, there has been an explosion of sequencing of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, making it the most widely sequenced virus in the history. Several databases and tools have been created to keep track of genome sequences and variants of the virus; most notably, the GISAID platform hosts millions of complete genome sequences, and it is continuously expanding every day. A challenging task is the development of fast and accurate tools that are able to distinguish between the different SARS-CoV-2 variants and assign them to a clade. In this article, we leverage the frequency chaos game representation (FCGR) and convolutional neural networks (CNNs) to develop an original method that learns how to classify genome sequences that we implement into CouGaR-g, a tool for the clade assignment problem on SARS-CoV-2 sequences. On a testing subset of the GISAID, CouGaR-g achieved an $96.29\%$ overall accuracy, while a similar tool, Covidex, obtained a $77,12\%$ overall accuracy. As far as we know, our method is the first using deep learning and FCGR for intraspecies classification. Furthermore, by using some feature importance methods, CouGaR-g allows to identify k-mers that match SARS-CoV-2 marker variants. By combining FCGR and CNNs, we develop a method that achieves a better accuracy than Covidex (which is based on random forest) for clade assignment of SARS-CoV-2 genome sequences, also thanks to our training on a much larger dataset, with comparable running times. Our method implemented in CouGaR-g is able to detect k-mers that capture relevant biological information that distinguishes the clades, known as marker variants. The trained models can be tested online providing a FASTA file (with 1 or multiple sequences) at https://huggingface.co/spaces/BIASLab/sars-cov-2-classification-fcgr. CouGaR-g is also available at https://github.com/AlgoLab/CouGaR-g under the GPL.

SPLACE: A tool to automatically SPLit, Align, and ConcatenatE genes for phylogenomic inference of several organisms.

The reconstruction of phylogenomic trees containing multiple genes is best achieved by using a supermatrix. The advent of NGS technology made it easier and cheaper to obtain multiple gene data in one sequencing run. When numerous genes and organisms are used in the phylogenomic analysis, it is difficult to organize all information and manually align the gene sequences to further concatenate them. This study describes SPLACE, a tool to automatically SPLit, Align, and ConcatenatE the genes of all species of interest to generate a supermatrix file, and consequently, a phylogenetic tree, while handling possible missing data. In our findings, SPLACE was the only tool that could automatically align gene sequences and also handle missing data; and, it required only a few minutes to produce a supermatrix FASTA file containing 83 aligned and concatenated genes from the chloroplast genomes of 270 plant species. It is an open-source tool and is publicly available at https://github.com/reinator/splace.

Sierra SARS-CoV-2 sequence and antiviral resistance analysis program.

Introduction: Although most laboratories are capable of employing established protocols to perform full-genome SARS-CoV-2 sequencing, many are unable to assess sequence quality, select appropriate mutation-detection thresholds, or report on the potential clinical significance of mutations in the targets of antiviral therapyMethods: We describe the technical aspects and benchmark the performance of Sierra SARS-CoV-2, a program designed to perform these functions on user-submitted FASTQ and FASTA sequence files and lists of Spike mutations. Sierra SARS-CoV-2 indicates which sequences contain an unexpectedly large number of unusual mutations and which mutations are associated with reduced susceptibility to clinical stage mAbs, the RdRP inhibitor remdesivir, or the Mpro inhibitor nirmatrelvirResults: To assess the performance of Sierra SARS-CoV-2 on FASTQ files, we applied it to 600 representative FASTQ sequences and compared the results to the COVID-19 EDGE program. To assess its performance on FASTA files, we applied it to nearly one million representative FASTA sequences and compared the results to the GISAID mutation annotation. To assess its performance on mutations lists, we applied it to 13,578 distinct Spike RBD mutation patterns and showed that exactly or partially matching annotations were available for 88% of patternsConclusion: Sierra SARS-CoV-2 leverages previously published data to improve the quality control of submitted viral genomic data and to provide functional annotation on the impact of mutations in the targets of antiviral SARS-CoV-2 therapy. The program can be found at https://covdb.stanford.edu/sierra/sars2/ and its source code at https://github.com/hivdb/sierra-sars2.

Multiplex in silico RAPD-Analysis for Genome Barcoding

In this work, we propose a new method for identifying organisms of multiplex polymerase chain reaction (PCR) with arbitrary primers in silico (multiplex in silico RAPD-analysis) for the unique identification of living organisms. The results of computer modeling search of possible primer annealing sites in genomic DNA, and their convertation into the genomic barcode format, are proposed. These data with information about used primers that can be unique for genomes. A comparative analysis of genomic barcodes of species of related plant species was carried out in order to classify them on the level of species and lines in the future. A pairwise analysis of the location of the same or similar amplicons within different subgenomes and genomes is presented. The genomes of wheat and Aegilops in FASTA files format are presented as the research samples. The proposed method makes possible to predict the success of the multiplex polymerase chain reaction using special primers in the laboratory. This technology allows the analysis of the entire genomic DNA, rather than fragments of the genome.

Specimods: A web-based tool for producing Genbank submission files for sequenced museum specimens

The expectation of published sequence data on Genbank and other sequence databases is not only a requirement for journal publication, but is incredibly valuable for continuing research in organismal evolution and ecology. The process of formatting and submitting sequence data from vouchered specimens is burdensome and tedious. Improperly formatted submissions lead to lengthy delays and frustrations on behalf of the submitters and reviewers. Specimods*1 is a new tool that utilizes the Global Biodiversity Information Facility (GBIF) API to produce formatted source modifier files that will facilitate the batch upload of specimen sequences to Genbank, using the sequence submission platform. Once a template CSV is populated with the specimen catalog numbers, institution codes, SeqID, sequence descriptions, and the sequences, Specimods will pull specimen metadata from GBIF to produce two files: a FASTA file with a complete definition line (SeqID, organism name, sequence title) and the sequences as well as a source modifier file containing specimen metadata. Currently, Specimods supports Internal Transcribed Spacer (ITS) (1, 2, or both), SSUrRNA_18s, and LSUrRNA_28s sequences. The application*2 uses an Express server and Node.js with data linked to a user account stored in a MySQL relational database. This presentation will demonstrate the tool using data from fungal specimens and show how submitting dozens of sequences with metadata can be accomplished in a matter of minutes.

Epitope-Evaluator: An interactive web application to study predicted T-cell epitopes.

Multiple immunoinformatic tools have been developed to predict T-cell epitopes from protein amino acid sequences for different major histocompatibility complex (MHC) alleles. These prediction tools output hundreds of potential peptide candidates which require further processing; however, these tools are either not graphical or not friendly for non-programming users. We present Epitope-Evaluator, a web tool developed in the Shiny/R framework to interactively analyze predicted T-cell epitopes. Epitope-Evaluator contains six tools providing the distribution of epitopes across a selected set of MHC alleles, the promiscuity and conservation of epitopes, and their density and location within antigens. Epitope-Evaluator requires as input the fasta file of protein sequences and the output prediction file coming out from any predictor. By choosing different cutoffs and parameters, users can produce several interactive plots and tables that can be downloaded as JPG and text files, respectively. Using Epitope-Evaluator, we found the HLA-B*40, HLA-B*27:05 and HLA-B*07:02 recognized fewer epitopes from the SARS-CoV-2 proteome than other MHC Class I alleles. We also identified shared epitopes between Delta, Omicron, and Wuhan Spike variants as well as variant-specific epitopes. In summary, Epitope-Evaluator removes the programming barrier and provides intuitive tools, allowing a straightforward interpretation and graphical representations that facilitate the selection of candidate epitopes for experimental evaluation. The web server Epitope-Evaluator is available at https://fuxmanlab.shinyapps.io/Epitope-Evaluator/

CaSilico: A versatile CRISPR package for in silico CRISPR RNA designing for Cas12, Cas13, and Cas14.

The efficiency of the CRISPR-Cas system is highly dependent on well-designed CRISPR RNA (crRNA). To facilitate the use of various types of CRISPR-Cas systems, there is a need for the development of computational tools to design crRNAs which cover different CRISPR-Cas systems with off-target analysis capability. Numerous crRNA design tools have been developed, but nearly all of them are dedicated to design crRNA for genome editing. Hence, we developed a tool matching the needs of both beginners and experts, named CaSilico, which was inspired by the limitations of the current crRNA design tools for designing crRNAs for Cas12, Cas13, and Cas14 CRISPR-Cas systems. This tool considers a comprehensive list of the principal rules that are not yet well described to design crRNA for these types. Using a list of important features such as mismatch tolerance rules, self-complementarity, GC content, frequency of cleaving base around the target site, target accessibility, and PFS (protospacer flanking site) or PAM (protospacer adjacent motif) requirement, CaSilico searches all potential crRNAs in a user-input sequence. Considering these features help users to rank all crRNAs for a sequence and make an informed decision about whether a crRNA is suited for an experiment or not. Our tool is sufficiently flexible to tune some key parameters governing the design of crRNA and identification of off-targets, which can lead to an increase in the chances of successful CRISPR-Cas experiments. CaSilico outperforms previous crRNA design tools in the following aspects: 1) supporting any reference genome/gene/transcriptome for which an FASTA file is available; 2) designing crRNAs that simultaneously target multiple sequences through conserved region detection among a set of sequences; 3) considering new CRISPR-Cas subtypes; and 4) reporting a list of different features for each candidate crRNA, which can help the user to select the best one. Given these capabilities, CaSilico addresses end-user concerns arising from the use of sophisticated bioinformatics algorithms and has a wide range of potential research applications in different areas, especially in the design of crRNA for pathogen diagnosis. CaSilico was successfully applied to design crRNAs for different genes in the SARS-CoV-2 genome, as some of the crRNAs have been experimentally tested in the previous studies.

Using HPV-meta for human papillomavirus RNA quality detection

In the era of cervical cancer elimination, accurate and validated pipelines to detect human papillomavirus are essential to elucidate and understand HPV association with human cancers. We aimed to provide an open-source pipeline, “HPV-meta”, to detect HPV transcripts in RNA sequencing data, including several steps to warn operators for possible viral contamination. The “HPV-meta” pipeline automatically performs several steps, starting with quality trimming, human genome filtering, HPV detection (blastx), cut-off settlement (10 reads and 690 bp coverage to make an HPV call) and finishing with fasta sequence generation for HPV positive samples. Fasta sequences can then be aligned to assess sequence diversity among HPV positive samples. All RNA sequencing files (n = 10,908) present in the cancer genome atlas (TCGA) were analyzed. “HPV-meta” identified 25 different HPV types being present in 488/10,904 specimens. Validation of results showed 99.98% agreement (10,902/10,904). Multiple alignment from fasta files warned about high sequence identity between several HPV 18 and 38 positive samples, whose contamination had previously been reported. The “HPV-meta” pipeline is a robust and validated pipeline that detects HPV in RNA sequencing data. Obtaining the fasta files enables contamination investigation, a non very rare occurrence in next generation sequencing.