Sequence Analysis Tools Research Articles

QuickBAM: a parallelized BAM file access API for high-throughput sequence analysis informatics.

In time-critical clinical settings, such as precision medicine, genomic data needs to be processed as fast as possible to arrive at data-informed treatment decisions in a timely fashion. While sequencing throughput has dramatically increased over the past decade, bioinformatics analysis throughput has not been able to keep up with the pace of computer hardware improvement, and consequently has now turned into the primary bottleneck. Modern computer hardware today is capable of much higher performance than current genomic informatics algorithms can typically utilize, therefore presenting opportunities for significant improvement of performance. Accessing the raw sequencing data from BAM files, e.g. is a necessary and time-consuming step in nearly all sequence analysis tools, however existing programming libraries for BAM access do not take full advantage of the parallel input/output capabilities of storage devices. In an effort to stimulate the development of a new generation of faster sequence analysis tools, we developed quickBAM, a software library to accelerate sequencing data access by exploiting the parallelism in commodity storage hardware currently widely available. We demonstrate that analysis software ported to quickBAM consistently outperforms their current versions, in some cases finishing an analysis in under 3 min while the original version took 1.5 h, using the same storage solution. Open source and freely available at https://gitlab.com/yiq/quickbam/, we envision that quickBAM will enable a new generation of high-performance informatics tools, either directly boosting their performance if they are currently data-access bottlenecked, or allow data-access to keep up with further optimizations in algorithms and compute techniques.

Expression of Heat Shock Protein 30 in Talaromyces marneffei during Phase Transition and in Response to Heat and Oxidative Stresses

OBJECTIVE Heat shock protein 30 (Hsp30) has been identified as an immunogenic, yeast phase-specific protein in Talaromyces marneffei. The purpose of this study was to investigate how the hsp30 gene and Hsp30 protein are expressed during phase transition and in response to heat and oxidative stress exposure. METHODS Several sequence analysis tools were employed to predict hsp30 control elements and to determine the subcellular localization of Hsp30. In the phase transition experiment, Talaromyces marneffei conidia were cultivated at two different temperatures, 25 °C and 37 °C. Subsequently, stress response tests were conducted by subjecting the yeast cells to heat at 42 °C and by treating them with hydrogen peroxide. The levels of the hsp30 transcript and its protein were measured using real-time RT-PCR and western immunoblot analysis, respectively. RESULTS The sequence analysis revealed the presence of heat response element (HRE), stress responsive element (STRE), and xenobiotic responsive element (XRE), which are typically involved in regulating hsp genes. A web-based tool predicted that Hsp30 protein is localized in cytoplasm, nucleus, and cell membrane. The hsp30 transcript and Hsp30 protein were highly clearly detected in both yeast cells and conidia. Furthermore, the hsp30 transcript in yeast cells was upregulated following heat shock at 42° C and exposure to hydrogen peroxide. These findings indicate that Hsp30 plays a crucial role in assisting the yeast phase of T. marneffei to cope with heat and oxidative stresses. CONCLUSIONS Hsp30 is a protein specific to the conidial and yeast phases of T. marneffei. It likely performs a conserved chaperone function during yeast growth and plays a significant role in stress response by mitigating protein aggregation issues. KEYWORDS Talaromyces marneffei, heat shock protein 30, expression

P-746 Carrier Screening analysis in more than 20.000 patients. Is complete analysis important vs genotyping analysis?

Abstract Study question How comprehensive gene sequencing analysis impacts on carrier screening (CS) results and couple risk reduction? Summary answer Comprehensive genetic sequencing analysis facilitates more accurate carrier rates and provides better information for final panel design and partner risk reduction. What is known already Since CS began in the 1970s, it has helped millions of couples at risk of affected offspring affected by genetic diseases.From the first panels for Ashkenazi Jewish to today’s pan-ethnic CS panels, genetic testing has improved from analysis of a few known pathogenic variants to full sequence analysis. The American College of Medical Genetics and Genomics (ACMG) published Standards and Guidelines for the Interpretation of Sequence Variants in 2015, providing an important tool for full sequence analysis for CS. CS analyses can report only already known mutations (genotyping) or screen for all variants with ACMG-guidelines and laboratory experience (full sequencing). Study design, size, duration This study includes 20774 patients screened with an expanded carrier screening panel of 301 genes, including autosomal recessive (AR) and X-linked (XL) disorders by next-generation sequencing (NGS). ACMG guidelines for variant interpretation were followed in 13954 patients and only pathogenic and likely pathogenic variants were reported. 6820 patients were analyzed with genotyping analysis. 15758 matching analyses were performed for couples and gamete donors. The study has been conducted between August 2018 and December 2022. Participants/materials, setting, methods DNA was extracted from saliva and blood samples and sequenced. The data obtained were processed by means of computer tools. The carrier frequency (CFR) was obtained from the full sequencing analyses. The observed CFRs was compared with the expected CFRs obtained from databases and literature.To define its similarity a range of +-25% was accepted as similar CFR. For matching analyses, high at-risk couples were divided for autosomal recessive diseases(both carriers) and X-linked diseases (female carriers). Main results and the role of chance For the 301 genes analyzed, 34%(103) had a similar CF than the expected one and, more importantly, 65%(198) showed different CFR. In some cases this difference is due to the lack of complete information on the CFR on bibliography and databases like the CFR associated a diseases with more than one gene associated or genes related with more than one disease, some of which with low clinical implications, usually underdiagnosed. One of the genes on which the CFR was not similar to the expected was SMN1. For this well-known gene, related to spinal muscular atrophy, the CFR was higher than expected, probably due to the analysis of the variants c.*3 + 80T>G(p.?),c.*211_*212del(p.?) that indicate a higher risk of 0 + 2 configuration. The patients with these variants and 2 SMN1-exon8 copies are at higher risk to be silent carriers, but they may not be carriers. For the 15758 performed, if one or both patients were analyzed by genotyping analysis, 3,6% (432/12055 matchings) identified had high risk of affected offspring for autosomal or X-Linked recessive diseases. When both patients were analyzed by sequencing, 6% (223/3703 matchings) were identified at high risk of affected offspring of the diseases analyzed allowing a higher identification of couples at risk. Limitations, reasons for caution The analysis was performed on 301 genes, in order to have a better understanding of CFR for panel designs or patients genetic counseling, a larger cohort of genes should be taken into account, these results show the importance of complete analysis to assess the carrier risks. Wider implications of the findings It is important to have wider genetic databases for CFR but also for variant pathogenicity in order to have better results on CFR analyses and panel designs, in order to provide proper genetic counseling to patients looking to improve the chances of healthy offspring. Trial registration number Not applicable

Exploring the Potential of GANs in Biological Sequence Analysis.

Biological sequence analysis is an essential step toward building a deeper understanding of the underlying functions, structures, and behaviors of the sequences. It can help in identifying the characteristics of the associated organisms, such as viruses, etc., and building prevention mechanisms to eradicate their spread and impact, as viruses are known to cause epidemics that can become global pandemics. New tools for biological sequence analysis are provided by machine learning (ML) technologies to effectively analyze the functions and structures of the sequences. However, these ML-based methods undergo challenges with data imbalance, generally associated with biological sequence datasets, which hinders their performance. Although various strategies are present to address this issue, such as the SMOTE algorithm, which creates synthetic data, however, they focus on local information rather than the overall class distribution. In this work, we explore a novel approach to handle the data imbalance issue based on generative adversarial networks (GANs), which use the overall data distribution. GANs are utilized to generate synthetic data that closely resembles real data, thus, these generated data can be employed to enhance the ML models' performance by eradicating the class imbalance problem for biological sequence analysis. We perform four distinct classification tasks by using four different sequence datasets (Influenza A Virus, PALMdb, VDjDB, Host) and our results illustrate that GANs can improve the overall classification performance.

Graph Theory in DNA Sequencing: Unveiling Genetic Patterns

Graph theory, a branch of mathematics that studies the properties and relationships of graphs, has emerged as a powerful tool in addressing the complexities of DNA sequencing. This paper highlights the application of graph theory in DNA sequencing and its implications in various aspects of genomics research. One fundamental concept in graph theory applied to DNA sequencing is the construction of the de Bruijn graph. This graph represents overlapping k-mers, subsequences of length k, as nodes, with edges connecting adjacent k-mers. By constructing and analyzing the de Bruijn graph, researchers can infer the underlying DNA sequence, detect errors, resolve repetitive regions, and identify structural variations in the genome. Graph algorithms such as Overlap path, Eulerian path and Hamiltonian path have been adapted to reconstruct complete DNA sequences from fragmented reads obtained through sequencing. These algorithms leverage the connectivity information present in the de Bruijn graph to traverse and assemble the reads, enabling the reconstruction of long DNA sequences accurately. the application of graph theory in DNA sequencing has revolutionized the field of genomics by providing powerful computational tools for DNA assembly, sequence analysis, and functional annotation. By leveraging graph theory concepts and algorithms, researchers can unravel the intricate information embedded within DNA sequences, leading to deeper insights into the genetic basis of life and its applications in various fields, including medicine, agriculture, and evolutionary biology. Future developments in graph-based algorithms and computational techniques hold promise for further enhancing our ability to unlock the secrets encoded within the vast realm of DNA sequences.

Cellsnake: a user-friendly tool for single cell RNA sequencing analysis

cellsnake: a user-friendly tool for single cell RNA sequencing analysis

Wastewater surveillance of SARS-CoV-2 genomic populations on a country-wide scale through targeted sequencing.

SARS-CoV-2 surveillance of viral populations in wastewater samples is recognized as a useful tool for monitoring epidemic waves and boosting health preparedness. Next generation sequencing of viral RNA isolated from wastewater is a convenient and cost-effective strategy to understand the molecular epidemiology of SARS-CoV-2 and provide insights on the population dynamics of viral variants at the community level. However, in low- and middle-income countries, isolated groups have performed wastewater monitoring and data has not been extensively shared in the scientific community. Here we report the results of monitoring the co-circulation and abundance of variants of concern (VOCs) of SARS-CoV-2 in Uruguay, a small country in Latin America, between November 2020-July 2021 using wastewater surveillance. RNA isolated from wastewater was characterized by targeted sequencing of the Receptor Binding Domain region within the spike gene. Two computational approaches were used to track the viral variants. The results of the wastewater analysis showed the transition in the overall predominance of viral variants in wastewater from No-VOCs to successive VOCs, in agreement with clinical surveillance from sequencing of nasal swabs. The mutations K417T, E484K and N501Y, that characterize the Gamma VOC, were detected as early as December 2020, several weeks before the first clinical case was reported. Interestingly, a non-synonymous mutation described in the Delta VOC, L452R, was detected at a very low frequency since April 2021 when using a recently described sequence analysis tool (SAM Refiner). Wastewater NGS-based surveillance of SARS-CoV-2 is a reliable and complementary tool for monitoring the introduction and prevalence of VOCs at a community level allowing early public health decisions. This approach allows the tracking of symptomatic and asymptomatic individuals, who are generally under-reported in countries with limited clinical testing capacity. Our results suggests that wastewater-based epidemiology can contribute to improving public health responses in low- and middle-income countries.

Research progress on the study of transcriptome-wide poly(A) tails in mammalian oocytes and early embryos.

During mammalian oocyte-to-embryo transition, before zygotic genome activation, the transcription in oocytes and embryos is silenced, so the post-transcriptional regulation of mRNA plays an essential role in this process. Poly(A) tail is an important post-transcriptional modification that affects mRNA metabolism and translation efficiency. With the development of sequencing technology and analysis tools, especially the methods based on third-generation sequencing technology, the length and composition of poly(A) tails can be accurately measured, greatly expanding our understanding of poly(A) tails in mammalian early embryonic development. This review focuses on the achievements of poly(A) tail sequencing methods and the research progress of poly(A) tail in regulating oocyte-to-embryo transition, discussing the future applications for the investigation of mammalian early embryonic development and infertility related diseases.

Distinguishing Puccinia striiformis f. sp. tritici Isolates Using Genomic Sequencing: A Case Study.

We are reporting on the utilization of high-throughput sequencing and different sequencing analysis tools to delineate identification of different isolates of the stripe rust fungal pathogen Puccinia striiformis f. sp. tritici (Pst). Different approaches are shown: utilization of rDNA sequences and random sequences that may be very useful to make sure that isolates belong to Pst and to distinguished closely related isolates. Identification of unique/lost sequences could lead to the identification of effectors associated with specific isolates.

Cellsnake: a user-friendly tool for single-cell RNA sequencing analysis.

Single-cell RNA sequencing (scRNA-seq) provides high-resolution transcriptome data to understand the heterogeneity of cell populations at the single-cell level. The analysis of scRNA-seq data requires the utilization of numerous computational tools. However, nonexpert users usually experience installation issues, a lack of critical functionality or batch analysis modes, and the steep learning curves of existing pipelines. We have developed cellsnake, a comprehensive, reproducible, and accessible single-cell data analysis workflow, to overcome these problems. Cellsnake offers advanced features for standard users and facilitates downstream analyses in both R and Python environments. It is also designed for easy integration into existing workflows, allowing for rapid analyses of multiple samples. As an open-source tool, cellsnake is accessible through Bioconda, PyPi, Docker, and GitHub, making it a cost-effective and user-friendly option for researchers. By using cellsnake, researchers can streamline the analysis of scRNA-seq data and gain insights into the complex biology of single cells.

Molecular characterization of HBB gene mutations in beta-thalassemia patients of Southern Iraq

Introduction and Aim: Beta-thalassemia is a serious inherited genetic disorder and an increasing health burden globally. Beta -thalassemia is caused by genetic globin abnormalities within the hemoglobin beta (HBB) gene. This study aimed to characterize the HBB gene mutations in beta -thalassemia among southern Iraqi patients.
 
 Materials and Methods: The study included 30 beta -thalassemia patients referred to the Thi-Qar Center for Genetic Diseases, Iraq and 15 control samples from a random group of apparently healthy individuals. Genomic DNA was isolated from blood sample collected from each individual. The DNA was amplified for specific regions of the HBB gene and the amplified products sequenced. The sequences generated were analysed for mutations using sequence analysis tools.
 
 Results: Molecular analysis revealed several mutations in the HBB gene including translocation, deletion and substitution mutations in the population tested positive for the beta -thalassemia trait.
 
 Conclusion: Thalassemia major is a serious concern in southern Iraq and therefore this study emphasizes a need for complete mutation profiling of the beta -globin gene as a strategy for screening of carriers within the population. Such examinations could be useful in pre-marital genetic counseling and for undertaking prevention and treatment measures.

LotuS2: an ultrafast and highly accurate tool for amplicon sequencing analysis

BackgroundAmplicon sequencing is an established and cost-efficient method for profiling microbiomes. However, many available tools to process this data require both bioinformatics skills and high computational power to process big datasets. Furthermore, there are only few tools that allow for long read amplicon data analysis. To bridge this gap, we developed the LotuS2 (less OTU scripts 2) pipeline, enabling user-friendly, resource friendly, and versatile analysis of raw amplicon sequences.ResultsIn LotuS2, six different sequence clustering algorithms as well as extensive pre- and post-processing options allow for flexible data analysis by both experts, where parameters can be fully adjusted, and novices, where defaults are provided for different scenarios.We benchmarked three independent gut and soil datasets, where LotuS2 was on average 29 times faster compared to other pipelines, yet could better reproduce the alpha- and beta-diversity of technical replicate samples. Further benchmarking a mock community with known taxon composition showed that, compared to the other pipelines, LotuS2 recovered a higher fraction of correctly identified taxa and a higher fraction of reads assigned to true taxa (48% and 57% at species; 83% and 98% at genus level, respectively). At ASV/OTU level, precision and F-score were highest for LotuS2, as was the fraction of correctly reported 16S sequences.ConclusionLotuS2 is a lightweight and user-friendly pipeline that is fast, precise, and streamlined, using extensive pre- and post-ASV/OTU clustering steps to further increase data quality. High data usage rates and reliability enable high-throughput microbiome analysis in minutes.AvailabilityLotuS2 is available from GitHub, conda, or via a Galaxy web interface, documented at http://lotus2.earlham.ac.uk/.6hTcPpJ8wvT9bQx45qQZLRVideo

Evolutionary origin and functional specialization of Dormancy-Associated MADS box (DAM) proteins in perennial crops

BackgroundBud dormancy is a phenological adaptation of temperate perennials that ensures survival under winter temperature conditions by ceasing growth and increasing cold hardiness. SHORT VEGETATIVE PHASE (SVP)-like factors, and particularly a subset of them named DORMANCY-ASSOCIATED MADS-BOX (DAM), are master regulators of bud dormancy in perennials, prominently Rosaceae crops widely adapted to varying environmental conditions.ResultsSVP-like proteins from recently sequenced Rosaceae genomes were identified and characterized using sequence, phylogenetic and synteny analysis tools. SVP-like proteins clustered in three clades (SVP1–3), with known DAM proteins located within SVP2 clade, which also included Arabidopsis AGAMOUS-LIKE 24 (AthAGL24). A more detailed study on these protein sequences led to the identification of a 15-amino acid long motif specific to DAM proteins, which affected protein heteromerization properties by yeast two-hybrid system in peach PpeDAM6, and the unexpected finding of predicted DAM-like genes in loquat, an evergreen species lacking winter dormancy. DAM gene expression in loquat trees was studied by quantitative PCR, associating with inflorescence development and growth in varieties with contrasting flowering behaviour.ConclusionsPhylogenetic, synteny analyses and heterologous overexpression in the model plant Arabidopsis thaliana supported three major conclusions: 1) DAM proteins might have emerged from the SVP2 clade in the Amygdaloideae subfamily of Rosaceae; 2) a short DAM-specific motif affects protein heteromerization, with a likely effect on DAM transcriptional targets and other functional features, providing a sequence signature for the DAM group of dormancy factors; 3) in agreement with other recent studies, DAM associates with inflorescence development and growth, independently of the dormancy habit.

Benchmark datasets for SARS-CoV-2 surveillance bioinformatics.

BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), has spread globally and is being surveilled with an international genome sequencing effort. Surveillance consists of sample acquisition, library preparation, and whole genome sequencing. This has necessitated a classification scheme detailing Variants of Concern (VOC) and Variants of Interest (VOI), and the rapid expansion of bioinformatics tools for sequence analysis. These bioinformatic tools are means for major actionable results: maintaining quality assurance and checks, defining population structure, performing genomic epidemiology, and inferring lineage to allow reliable and actionable identification and classification. Additionally, the pandemic has required public health laboratories to reach high throughput proficiency in sequencing library preparation and downstream data analysis rapidly. However, both processes can be limited by a lack of a standardized sequence dataset.MethodsWe identified six SARS-CoV-2 sequence datasets from recent publications, public databases and internal resources. In addition, we created a method to mine public databases to identify representative genomes for these datasets. Using this novel method, we identified several genomes as either VOI/VOC representatives or non-VOI/VOC representatives. To describe each dataset, we utilized a previously published datasets format, which describes accession information and whole dataset information. Additionally, a script from the same publication has been enhanced to download and verify all data from this study.ResultsThe benchmark datasets focus on the two most widely used sequencing platforms: long read sequencing data from the Oxford Nanopore Technologies platform and short read sequencing data from the Illumina platform. There are six datasets: three were derived from recent publications; two were derived from data mining public databases to answer common questions not covered by published datasets; one unique dataset representing common sequence failures was obtained by rigorously scrutinizing data that did not pass quality checks. The dataset summary table, data mining script and quality control (QC) values for all sequence data are publicly available on GitHub: https://github.com/CDCgov/datasets-sars-cov-2.DiscussionThe datasets presented here were generated to help public health laboratories build sequencing and bioinformatics capacity, benchmark different workflows and pipelines, and calibrate QC thresholds to ensure sequencing quality. Together, improvements in these areas support accurate and timely outbreak investigation and surveillance, providing actionable data for pandemic management. Furthermore, these publicly available and standardized benchmark data will facilitate the development and adjudication of new pipelines.

NtHash2: recursive spaced seed hashing for nucleotide sequences.

MotivationSpaced seeds are robust alternatives to k-mers in analyzing nucleotide sequences with high base mismatch rates. Hashing is also crucial for efficiently storing abundant sequence data. Here, we introduce ntHash2, a fast algorithm for spaced seed hashing that can be integrated into various bioinformatics tools for efficient sequence analysis with applications in genome research.ResultsntHash2 is up to 2.1× faster at hashing various spaced seeds than the previous version and 3.8× faster than conventional hashing algorithms with naïve adaptation. Additionally, we reduced the collision rate of ntHash for longer k-mer lengths and improved the uniformity of the hash distribution by modifying the canonical hashing mechanism.Availability and implementationntHash2 is freely available online at github.com/bcgsc/ntHash under an MIT license.Supplementary information Supplementary data are available at Bioinformatics online.

MOCHI: a comprehensive cross-platform tool for amplicon-based microbiota analysis.

MotivationMicrobiota analyses have important implications for health and science. These analyses make use of 16S/18S rRNA gene sequencing to identify taxa and predict species diversity. However, most available tools for analyzing microbiota data require adept programming skills and in-depth statistical knowledge for proper implementation. While long-read amplicon sequencing can lead to more accurate taxa predictions and is quickly becoming more common, practitioners have no easily accessible tools with which to perform their analyses.ResultsWe present MOCHI, a GUI tool for microbiota amplicon sequencing analysis. MOCHI preprocesses sequences, assigns taxonomy, identifies different abundant species and predicts species diversity and function. It takes either taxonomic count table or FASTQ of partial 16S/18S rRNA or full-length 16S rRNA gene as input. It performs analyses in real time and visualizes data in both tabular and graphical formats.Availability and implementationMOCHI can be installed to run locally or accessed as a web tool at https://mochi.life.nctu.edu.tw.Supplementary information Supplementary data are available at Bioinformatics online.

Deep Phenotyping of Chinese Electronic Health Records by Recognizing Linguistic Patterns of Phenotypic Narratives With a Sequence Motif Discovery Tool: Algorithm Development and Validation.

BackgroundPhenotype information in electronic health records (EHRs) is mainly recorded in unstructured free text, which cannot be directly used for clinical research. EHR-based deep-phenotyping methods can structure phenotype information in EHRs with high fidelity, making it the focus of medical informatics. However, developing a deep-phenotyping method for non-English EHRs (ie, Chinese EHRs) is challenging. Although numerous EHR resources exist in China, fine-grained annotation data that are suitable for developing deep-phenotyping methods are limited. It is challenging to develop a deep-phenotyping method for Chinese EHRs in such a low-resource scenario.ObjectiveIn this study, we aimed to develop a deep-phenotyping method with good generalization ability for Chinese EHRs based on limited fine-grained annotation data.MethodsThe core of the methodology was to identify linguistic patterns of phenotype descriptions in Chinese EHRs with a sequence motif discovery tool and perform deep phenotyping of Chinese EHRs by recognizing linguistic patterns in free text. Specifically, 1000 Chinese EHRs were manually annotated based on a fine-grained information model, PhenoSSU (Semantic Structured Unit of Phenotypes). The annotation data set was randomly divided into a training set (n=700, 70%) and a testing set (n=300, 30%). The process for mining linguistic patterns was divided into three steps. First, free text in the training set was encoded as single-letter sequences (P: phenotype, A: attribute). Second, a biological sequence analysis tool—MEME (Multiple Expectation Maximums for Motif Elicitation)—was used to identify motifs in the single-letter sequences. Finally, the identified motifs were reduced to a series of regular expressions representing linguistic patterns of PhenoSSU instances in Chinese EHRs. Based on the discovered linguistic patterns, we developed a deep-phenotyping method for Chinese EHRs, including a deep learning–based method for named entity recognition and a pattern recognition–based method for attribute prediction.ResultsIn total, 51 sequence motifs with statistical significance were mined from 700 Chinese EHRs in the training set and were combined into six regular expressions. It was found that these six regular expressions could be learned from a mean of 134 (SD 9.7) annotated EHRs in the training set. The deep-phenotyping algorithm for Chinese EHRs could recognize PhenoSSU instances with an overall accuracy of 0.844 on the test set. For the subtask of entity recognition, the algorithm achieved an F1 score of 0.898 with the Bidirectional Encoder Representations from Transformers–bidirectional long short-term memory and conditional random field model; for the subtask of attribute prediction, the algorithm achieved a weighted accuracy of 0.940 with the linguistic pattern–based method.ConclusionsWe developed a simple but effective strategy to perform deep phenotyping of Chinese EHRs with limited fine-grained annotation data. Our work will promote the second use of Chinese EHRs and give inspiration to other non–English-speaking countries.

CAVES: A Novel Tool for Comparative Analysis of Variant Epitope Sequences.

In silico methods for immune epitope prediction have become essential for vaccine and therapeutic design, but manual intra-species comparison of putative epitopes remains challenging and subject to human error. Created initially for analyzing SARS-CoV-2 variants of concern, comparative analysis of variant epitope sequences (CAVES) is a novel tool designed to carry out rapid comparative analyses of epitopes amongst closely related pathogens, substantially reducing the required time and user workload. CAVES applies a two-level analysis approach. The Level-one (L1) analysis compares two epitope prediction files, and the Level-two (L2) analysis incorporates search results from the IEDB database of experimentally confirmed epitopes. Both L1 and L2 analyses sort epitopes into categories of exact matches, partial matches, or novel epitopes based on the degree to which they match with peptides from the compared file. Furthermore, CAVES uses positional sequence data to improve its accuracy and speed, taking only a fraction of the time required by manual analyses and minimizing human error. CAVES is widely applicable for evolutionary analyses and antigenic comparisons of any closely related pathogen species. CAVES is open-source software that runs through a graphical user interface on Windows operating systems, making it widely accessible regardless of coding expertise. The CAVES source code and test dataset presented here are publicly available on the CAVES GitHub page.

RSAT 2022: regulatory sequence analysis tools.

RSAT (Regulatory Sequence Analysis Tools) enables the detection and the analysis of cis-regulatory elements in genomic sequences. This software suite performs (i) de novo motif discovery (including from genome-wide datasets like ChIP-seq/ATAC-seq) (ii) genomic sequences scanning with known motifs, (iii) motif analysis (quality assessment, comparisons and clustering), (iv) analysis of regulatory variations and (v) comparative genomics. RSAT comprises 50 tools. Six public Web servers (including a teaching server) are offered to meet the needs of different biological communities. RSAT philosophy and originality are: (i) a multi-modal access depending on the user needs, through web forms, command-line for local installation and programmatic web services, (ii) a support for virtually any genome (animals, bacteria, plants, totalizing over 10 000 genomes directly accessible). Since the 2018 NAR Web Software Issue, we have developed a large REST API, extended the support for additional genomes and external motif collections, enhanced some tools and Web forms, and developed a novel tool that builds or refine gene regulatory networks using motif scanning (network-interactions). The RSAT website provides extensive documentation, tutorials and published protocols. RSAT code is under open-source license and now hosted in GitHub. RSAT is available at http://www.rsat.eu/.

Differential Etv2 threshold requirement for endothelial and erythropoietic development.

SUMMARYEndothelial and erythropoietic lineages arise from a common developmental progenitor. Etv2 is a master transcriptional regulator required for the development of both lineages. However, the mechanisms through which Etv2 initiates the gene-regulatory networks (GRNs) for endothelial and erythropoietic specification and how the two GRNs diverge downstream of Etv2 remain incompletely understood. Here, by analyzing a hypomorphic Etv2 mutant, we demonstrate different threshold requirements for initiation of the downstream GRNs for endothelial and erythropoietic development. We show that Etv2 functions directly in a coherent feedforward transcriptional network for vascular endothelial development, and a low level of Etv2 expression is sufficient to induce and sustain the endothelial GRN. In contrast, Etv2 induces the erythropoietic GRN indirectly via activation of Tal1, which requires a significantly higher threshold of Etv2 to initiate and sustain erythropoietic development. These results provide important mechanistic insight into the divergence of the endothelial and erythropoietic lineages.