Bioinformatics Research Articles

Development of an automated amplicon-based next-generation sequencing pipeline for rapid detection of bacteria and fungi directly from clinical specimens.

To our knowledge, this represents the first application of an automated sequencing and bioinformatics pipeline in an exclusively pediatric population. Next-generation sequencing is time-consuming, labor-intensive, and requires experienced personnel; perhaps contributing to hesitancy among clinical laboratories to adopt such a test. Here, we report a strong case for use by removing these barriers through near-total automation of our sequencing pipeline.

Split Pool Ligation-based Single-cell Transcriptome sequencing (SPLiT-seq) data processing pipeline comparison

BackgroundSingle-cell sequencing techniques are revolutionizing every field of biology by providing the ability to measure the abundance of biological molecules at a single-cell resolution. Although single-cell sequencing approaches have been developed for several molecular modalities, single-cell transcriptome sequencing is the most prevalent and widely applied technique. SPLiT-seq (split-pool ligation-based transcriptome sequencing) is one of these single-cell transcriptome techniques that applies a unique combinatorial-barcoding approach by splitting and pooling cells into multi-well plates containing barcodes. This unique approach required the development of dedicated computational tools to preprocess the data and extract the count matrices. Here we compare eight bioinformatic pipelines (alevin-fry splitp, LR-splitpipe, SCSit, splitpipe, splitpipeline, SPLiTseq-demultiplex, STARsolo and zUMI) that have been developed to process SPLiT-seq data. We provide an overview of the tools, their computational performance, functionality and impact on downstream processing of the single-cell data, which vary greatly depending on the tool used.ResultsWe show that STARsolo, splitpipe and alevin-fry splitp can all handle large amount of data within reasonable time. In contrast, the other five pipelines are slow when handling large datasets. When using smaller dataset, cell barcode results are similar with the exception of SPLiTseq-demultiplex and splitpipeline. LR-splitpipe that is originally designed for processing long-read sequencing data is the slowest of all pipelines. Alevin-fry produced different down-stream results that are difficult to interpret. STARsolo functions nearly identical to splitpipe and produce results that are highly similar to each other. However, STARsolo lacks the function to collapse random hexamer reads for which some additional coding is required.ConclusionOur comprehensive comparative analysis aids users in selecting the most suitable analysis tool for efficient SPLiT-seq data processing, while also detailing the specific prerequisites for each of these pipelines. From the available pipelines, we recommend splitpipe or STARSolo for SPLiT-seq data analysis.

A process to reanalyze clinical DNA sequencing data for biomarker matching in the Lung-MAP Master Protocol.

For cancer clinical trials that require central confirmation of tumor genomic profiling, exhaustion of tissue from standard-of-care testing may prevent enrollment. For Lung-MAP, a master protocol that requires results from a defined centralized clinical trial assay to assign patients to a therapeutic substudy, we developed a process to repurpose existing commercial vendor raw genomic data for eligibility: genomic data reanalysis (GDR). Molecular results for substudy assignment were successfully generated for 369 of the first 374 patients (98.7%) using GDR for Lung-MAP, with a median time from request to result of 9 days. During the same period, 691 of 791 (87.4%) tissue samples received successfully yielded results, in a median of 14 days beyond sample acquisition. GDR is a scalable bioinformatic pipeline that expedites reanalysis of existing data for clinical trials in which validated integral biomarker testing is required for participation.

Mutation mapping of a variegated EMS tomato reveals an FtsH-like protein precursor potentially causing patches of four phenotype classes in the leaves with distinctive internal morphology

BackgroundLeaf variegation is an intriguing phenomenon observed in many plant species. However, questions remain on its mechanisms causing patterns of different colours. In this study, we describe a tomato plant detected in an M2 population of EMS mutagenised seeds, showing variegated leaves with sectors of dark green (DG), medium green (MG), light green (LG) hues, and white (WH). Cells and tissues of these classes, along with wild-type tomato plants, were studied by light, fluorescence, and transmission electron microscopy. We also measured chlorophyll a/b and carotene and quantified the variegation patterns with a machine-learning image analysis tool. We compared the genomes of pooled plants with wild-type-like and mutant phenotypes in a segregating F2 population to reveal candidate genes responsible for the variegation.ResultsA genetic test demonstrated a recessive nuclear mutation caused the variegated phenotype. Cross-sections displayed distinct anatomy of four-leaf phenotypes, suggesting a stepwise mesophyll degradation. DG sectors showed large spongy layers, MG presented intercellular spaces in palisade layers, and LG displayed deformed palisade cells. Electron photomicrographs of those mesophyll cells demonstrated a gradual breakdown of the chloroplasts. Chlorophyll a/b and carotene were proportionally reduced in the sectors with reduced green pigments, whereas white sectors have hardly any of these pigments. The colour segmentation system based on machine-learning image analysis was able to convert leaf variegation patterns into binary images for quantitative measurements. The bulk segregant analysis of pooled wild-type-like and variegated progeny enabled the identification of SNP and InDels via bioinformatic analysis. The mutation mapping bioinformatic pipeline revealed a region with three candidate genes in chromosome 4, of which the FtsH-like protein precursor (LOC100037730) carries an SNP that we consider the causal variegated phenotype mutation. Phylogenetic analysis shows the candidate is evolutionary closest to the Arabidopsis VAR1. The synonymous mutation created by the SNP generated a miRNA binding site, potentially disrupting the photoprotection mechanism and thylakoid development, resulting in leaf variegation.ConclusionWe described the histology, anatomy, physiology, and image analysis of four classes of cell layers and chloroplast degradation in a tomato plant with a variegated phenotype. The genomics and bioinformatics pipeline revealed a VAR1-related FtsH mutant, the first of its kind in tomato variegation phenotypes. The miRNA binding site of the mutated SNP opens the way to future studies on its epigenetic mechanism underlying the variegation.

Inventorizing marine biodiversity using eDNA data from Indonesian coral reefs: comparative high throughput analysis using different bioinformatic pipelines

Inventorizing marine biodiversity using eDNA data from Indonesian coral reefs: comparative high throughput analysis using different bioinformatic pipelines

Comparison of qPCR and metagenomic sequencing methods for quantifying antibiotic resistance genes in wastewater.

Surveillance methods of circulating antibiotic resistance genes (ARGs) are of utmost importance in order to tackle what has been described as one of the greatest threats to humanity in the 21st century. In order to be effective, these methods have to be accurate, quickly deployable, and scalable. In this study, we compare metagenomic shotgun sequencing (TruSeq DNA sequencing) of wastewater samples with a state-of-the-art PCR-based method (Resistomap HT-qPCR) on four wastewater samples that were taken from hospital, industrial, urban and rural areas. ARGs that confer resistance to 11 antibiotic classes have been identified in these wastewater samples using both methods, with the most abundant observed classes of ARGs conferring resistance to aminoglycoside, multidrug-resistance (MDR), macrolide-lincosamide-streptogramin B (MLSB), tetracycline and beta-lactams. In comparing the methods, we observed a strong correlation of relative abundance of ARGs obtained by the two tested methods for the majority of antibiotic classes. Finally, we investigated the source of discrepancies in the results obtained by the two methods. This analysis revealed that false negatives were more likely to occur in qPCR due to mutated primer target sites, whereas ARGs with incomplete or low coverage were not detected by the sequencing method due to the parameters set in the bioinformatics pipeline. Indeed, despite the good correlation between the methods, each has its advantages and disadvantages which are also discussed here. By using both methods together, a more robust ARG surveillance program can be established. Overall, the work described here can aid wastewater treatment plants that plan on implementing an ARG surveillance program.

Abstract LB185: High resolution telomere measurements in human cancer and aging using long-read nanopore sequencing

Abstract Telomere length is an important biomarker of cellular replicative potential and aging, but existing measurement methods are limited in resolution and accuracy. During human carcinogenesis, telomeres shorten profoundly in the earliest stages that precede tumor invasion. Telomere shortening can either halt cancer progression by inducing senescence and cell death or can promote cancer development by destabilizing chromosomes and generating chromosomal rearrangements. Stabilization of telomeres, typically by the upregulation of telomerase, is required for progression of nearly all human cancers. Here, we deploy digital telomere measurement by nanopore sequencing to understand how distributions of human telomere length change during aging and following carcinogenesis. We measure telomere attrition and de novo elongation with unprecedented resolution in genetically defined populations of human stem cells, in cells from the peripheral blood of healthy individuals and patients with genetic defects in telomere maintenance, cancer patients, and cancer cell lines. We find that human aging is accompanied by a progressive loss of long telomeres and an accumulation of shorter telomeres in white blood cells. In patients with inherited defects in telomere maintenance, the accumulation of short telomeres is more pronounced and correlates with phenotypic severity. We apply machine learning to train a binary classification model that distinguishes healthy individuals from those with telomere biology disorders. Next, we investigated the telomere lengths of a cohort of twenty colorectal tumors with patient-matched healthy colonic epithelia and found that two-thirds of colorectal tumors have telomere length distributions significantly shorter than the surrounding normal tissue. Finally, we explore the utility of telomere length distributions in cancer cell lines as clinically actionable disease biomarkers. This sequencing and bioinformatic pipeline will advance our understanding of telomere maintenance mechanisms and the use of telomere length as a clinical biomarker of aging and disease. Citation Format: Santiago Sanchez, Jessica Gu, Anudeep Golla, William Shomali, Annika Martin, Dirk Hockemeyer, Sharon Savage, Steven Artandi. High resolution telomere measurements in human cancer and aging using long-read nanopore sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB185.

Abstract LB381: Plasma microbiota as novel biomarkers of early epithelial ovarian cancer detection

Abstract Background: Ovarian cancer accounts for 2% of all female cancers in the United States yet it is the 5th leading cancer death among women. Advanced epithelial ovarian cancer (EOC) is associated with an overall survival rate of 30% but can be cured in up to 90% of cases if diagnosed early when the disease is still limited to the ovaries and fallopian tubes. While there is no effective screening method for EOC, developing novel and minimally invasive clinical tests is paramount to identify women with early disease as well as those who are at increased risk. Plasma-based microbiota signatures specific to EOC could become leading biomarkers of early disease in the field that complement existing clinical tools. Methods: We utilized an established institutional biobank to access 180 biobanked plasma samples from: (1) women with EOC; (2) women with non-EOC solid tumors; (3) women undergoing gynecologic-related procedures and non-EOC surgeries; and (4) healthy controls. Batch 1 included 96 plasma samples (n=24 per group) and Batch 2 included 84 samples (n=19 per group and 8 repeat EOC samples). Microbial profiles of plasma specimens were assessed using an amplicon-based sequencing method that targets the 16S rRNA gene in the bacterial genome to study bacterial phylogeny and taxonomy in diseased and healthy women. A stringent bioinformatic pipeline was applied to filter out suspicious bacterial contaminants at the genus level. Bacterial decontamination procedures in the pipeline included evaluating repeat plasma samples of EOC cases and sampling nucleic acid isolation kit reagents to serve as negative controls (only in Batch 2) . Differential abundances of the plasma microbiota among the groups were assessed using analysis of compositions of microbiomes (ANCOM) as implemented in R software package ANCOM-BC. Results: The 16S rRNA gene sequencing identified, classified, and quantified 564 bacterial genera in the circulation of women diagnosed with EOC, non-EOC solid tumors, benign gynecologic conditions, and healthy controls. Bioinformatic decontamination analysis removed bacteria: (1) with differing bacterial abundances between batches; (2) with higher bacteria in negative controls; (3) with differing bacterial abundances in repeat samples; and (4) known as contaminants in the literature that resulted in the removal of 225 high-risk bacterial contaminants. A total of 339 high quality bacteria remained for evaluation. Because we did not collect negative controls in Batch 1, only Batch 2 plasma samples (n=84; 27 in EOC group; 19 in each other group) were included in the final analysis. Women with EOC, which were primarily of the high-grade serous ovarian carcinoma histologic subtype had significantly distinct differential abundances of genera Brevibacterium (p <0.001), Chloronema (p <0.001), Facklamia (p <0.001), Zymomonas (p <0.001), and Sutterella (p <0.001) when compared to women with non-EOC solid tumors, benign gynecologic conditions, and healthy controls. Conclusions: These findings suggest that plasma EOC-associated bacteria could be exploited in the development of a microbial-based blood test for the diagnosis and early detection of EOC in routine practice settings. Citation Format: Diane E. Mahoney, Prabhakar Chalise, Dong Pei, Rachel Griffard, Trisha Home, Harsh Pathak, Shahid Umar, Andrew K. Godwin. Plasma microbiota as novel biomarkers of early epithelial ovarian cancer detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB381.

Detection of SARS-COV-2 variants and their proportions in wastewater samples using next-generation sequencing in Finland

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants may have different characteristics, e.g., in transmission, mortality, and the effectiveness of vaccines, indicating the importance of variant detection at the population level. Wastewater-based surveillance of SARS-CoV-2 RNA fragments has been shown to be an effective way to monitor the COVID-19 pandemic at the population level. Wastewater is a complex sample matrix affected by environmental factors and PCR inhibitors, causing insufficient coverage in sequencing, for example. Subsequently, results where part of the genome does not have sufficient coverage are not uncommon. To identify variants and their proportions in wastewater over time, we utilized next-generation sequencing with the ARTIC Network's primer set and bioinformatics pipeline to evaluate the presence of variants in partial genome data. Based on the wastewater data from November 2021 to February 2022, the Delta variant was dominant until mid-December in Helsinki, Finland’s capital, and thereafter in late December 2022 Omicron became the most common variant. At the same time, the Omicron variant of SARS-CoV-2 outcompeted the previous Delta variant in Finland in new COVID-19 cases. The SARS-CoV-2 variant findings from wastewater are in agreement with the variant information obtained from the patient samples when visually comparing trends in the sewerage network area. This indicates that the sequencing of wastewater is an effective way to monitor temporal and spatial trends of SARS-CoV-2 variants at the population level.

Experimental and analytical pipeline for sub-genomic RNA landscape of coronavirus by Nanopore sequencer.

Coronaviruses (CoVs), including severe acute respiratory syndrome coronavirus 2, can infect a variety of mammalian and avian hosts with significant medical and economic consequences. During the life cycle of CoV, a coordinated series of subgenomic RNAs, including canonical subgenomic messenger RNA and non-canonical defective viral genomes (DVGs), are generated with different biological implications. Studies that adopted the Nanopore sequencer (ONT) to investigate the landscape and dynamics of viral RNA subgenomic transcriptomes applied arbitrary bioinformatics parameters without justification or experimental validation. The current study used bovine coronavirus (BCoV), which can be performed under biosafety level 2 for library construction and experimental validation using traditional colony polymerase chain reaction and Sanger sequencing. Four different ONT protocols, including RNA direct and cDNA direct sequencing with or without exonuclease treatment, were used to generate RNA transcriptomic libraries from BCoV-infected cell lysates. Through rigorously examining the k-mer, gap size, segment size, and bin size, the optimal cutoffs for the bioinformatic pipeline were determined to remove the sequence noise while keeping the informative DVG reads. The sensitivity and specificity of identifying DVG reads using the proposed pipeline can reach 82.6% and 99.6% under the k-mer size cutoff of 15. Exonuclease treatment reduced the abundance of RNA transcripts; however, it was not necessary for future library preparation. Additional recovery of clipped BCoV nucleotide sequences with experimental validation expands the landscape of the CoV discontinuous RNA transcriptome, whose biological function requires future investigation. The results of this study provide the benchmarks for library construction and bioinformatic parameters for studying the discontinuous CoV RNA transcriptome.IMPORTANCEFunctional defective viral genomic RNA, containing all the cis-acting elements required for translation or replication, may play different roles in triggering cell innate immune signaling, interfering with the canonical subgenomic messenger RNA transcription/translation or assisting in establishing persistence infection. This study does not only provide benchmarks for library construction and bioinformatic parameters for studying the discontinuous coronavirus RNA transcriptome but also reveals the complexity of the bovine coronavirus transcriptome, whose functional assays will be critical in future studies.

Ultrasensitive sequencing of STR markers utilizing unique molecular identifiers and the SiMSen-Seq method

Massively parallel sequencing (MPS) is increasingly applied in forensic short tandem repeat (STR) analysis. The presence of stutter artefacts and other PCR or sequencing errors in the MPS-STR data partly limits the detection of low DNA amounts, e.g., in complex mixtures. Unique molecular identifiers (UMIs) have been applied in several scientific fields to reduce noise in sequencing. UMIs consist of a stretch of random nucleotides, a unique barcode for each starting DNA molecule, that is incorporated in the DNA template using either ligation or PCR. The barcode is used to generate consensus reads, thus removing errors. The SiMSen-Seq (Simple, multiplexed, PCR-based barcoding of DNA for sensitive mutation detection using sequencing) method relies on PCR-based introduction of UMIs and includes a sophisticated hairpin design to reduce unspecific primer binding as well as PCR protocol adjustments to further optimize the reaction. In this study, SiMSen-Seq is applied to develop a proof-of-concept seven STR multiplex for MPS library preparation and an associated bioinformatics pipeline. Additionally, machine learning (ML) models were evaluated to further improve UMI allele calling. Overall, the seven STR multiplex resulted in complete detection and concordant alleles for 47 single-source samples at 1ng input DNA as well as for low-template samples at 62.5pg input DNA. For twelve challenging mixtures with minor contributions of 10pg to 150pg and ratios of 1% to 15% relative to the major donor, 99.2% of the expected alleles were detected by applying the UMIs in combination with an ML filter. The main impact of UMIs was a substantially lowered number of artefacts as well as reduced stutter ratios, which were generally below 5% of the parental allele. In conclusion, UMI-based STR sequencing opens new means for improved analysis of challenging crime scene samples including complex mixtures.

A systematic analyses of different bioinformatics pipelines for genomic data and its impact on deep learning models for chromatin loop prediction.

Genomic data analysis has witnessed a surge in complexity and volume, primarily driven by the advent of high-throughput technologies. In particular, studying chromatin loops and structures has become pivotal in understanding gene regulation and genome organization. This systematic investigation explores the realm of specialized bioinformatics pipelines designed specifically for the analysis of chromatin loops and structures. Our investigation incorporates two protein (CTCF and Cohesin) factor-specific loop interaction datasets from six distinct pipelines, amassing a comprehensive collection of 36 diverse datasets. Through a meticulous review of existing literature, we offer a holistic perspective on the methodologies, tools and algorithms underpinning the analysis of this multifaceted genomic feature. We illuminate the vast array of approaches deployed, encompassing pivotal aspects such as data preparation pipeline, preprocessing, statistical features and modelling techniques. Beyond this, we rigorously assess the strengths and limitations inherent in these bioinformatics pipelines, shedding light on the interplay between data quality and the performance of deep learning models, ultimately advancing our comprehension of genomic intricacies.

Using nanopore sequencing to identify bacterial infection in joint replacements: a preliminary study.

This project investigates if third-generation genomic sequencing can be used to identify the species of bacteria causing prosthetic joint infections (PJIs) at the time of revision surgery. Samples of prosthetic fluid were taken during revision surgery from patients with known PJIs. Samples from revision surgeries from non-infected patients acted as negative controls. Genomic sequencing was performed using the MinION device and the rapid sequencing kit from Oxford Nanopore Technologies. Bioinformatic analysis pipelines to identify bacteria included Basic Local Alignment Search Tool, Kraken2 and MinION Detection Software, and the results were compared with standard of care microbiological cultures. Furthermore, there was an attempt to predict antibiotic resistance using computational tools including ResFinder, AMRFinderPlus and Comprehensive Antibiotic Resistance Database. Bacteria identified using microbiological cultures were successfully identified using bioinformatic analysis pipelines. Nanopore sequencing and genomic classification could be completed in the time it takes to perform joint revision surgery (2-3h). Genomic sequencing in this study was not able to predict antibiotic resistance in this time frame, this is thought to be due to a short-read length and low read depth. It can be concluded that genomic sequencing can be useful to identify bacterial species in infected joint replacements. However, further work is required to investigate if it can be used to predict antibiotic resistance within clinically relevant timeframes.

Unlocking Secrets: Bioinformatics' Impact on Forensic Bio-Examinations

Bioinformatics (BI) is a diverse and interdisciplinary domain that studies biological data through computational techniques. Its rapid technological progress has led to a significant increase in biological data production, sparking a notable expansion in BI research and application. In recent years, the convergence between BI and forensic science has become increasingly evident, with the growing use of BI tools and techniques in forensic investigations. BI is not only extensively used in biomedical research to analyse biological data and forecast gene regulation but also in the development of drugs targeting disorders linked to epigenetic alterations. It merges principles from various fields like biology, computer science, and statistics to process extensive omics data, create databases, predict structures, and model metabolic processes. The impact of BI on biomedical research is substantial, aiding in data processing, prediction, and visualisation. Moreover, BI is increasingly important in analysing data generated through Sanger-based electrophoresis methods and massively parallel sequencing in forensic DNA analysis, demonstrating its practical application in the field. Therefore, BI plays a critical role in forensic identification, searching databases side by side, forensic DNA phenotyping and biographic ancestry, forensic genetic genealogy, and forensic epigenomics. An overview of different bioinformatic methodologies employed in forensic applications, exploring approaches for managing data and investigating trend analysis strategies within forensic contexts, is discussed. Additionally, it investigates data management strategies. The primary objective was to explore how BI facilitates forensic BI's use, integration, and impact. Utilising a systematic literature review and content analysis, the study examined peer-reviewed articles, conference proceedings, and reports from critical databases like Google Scholar and Scopus, focusing on publications from 2010 to 2024. Key findings highlight the significant advancements in genomic sequencing technologies and their application in personalised therapeutics. Forensic scientists can improve forensic exams' accuracy, reliability, and efficiency by using BI tools, metrics, and efficient data management procedures.

Circular extrachromosomal DNA in Euglena gracilis under normal and stress conditions

Extrachromosomal circular DNA (eccDNA) enhances genomic plasticity, augmenting its coding and regulatory potential. Advances in high-throughput sequencing have enabled the investigation of these structural variants. Although eccDNAs have been investigated in numerous taxa, they remained understudied in euglenids. Therefore, we examined eccDNAs predicted from Illumina sequencing data of Euglena gracilis Z SAG 1224–5/25, grown under optimal photoperiod and exposed to UV irradiation. We identified approximately 1000 unique eccDNA candidates, about 20% of which were shared across conditions. We also observed a significant enrichment of mitochondrially encoded eccDNA in the UV-irradiated sample. Furthermore, we found that the heterogeneity of eccDNA was reduced in UV-exposed samples compared to cells that were grown in optimal conditions. Hence, eccDNA appears to play a role in the response to oxidative stress in Euglena, as it does in other studied organisms. In addition to contributing to the understanding of Euglena genomes, our results contribute to the validation of bioinformatics pipelines on a large, non-model genome.

VoyAGEr, a free web interface for the analysis of age-related gene expression alterations in human tissues

We herein introduce voyAGEr, an online graphical interface to explore age-related gene expression alterations in 49 human tissues. voyAGEr offers a visualisation and statistical toolkit for the finding and functional exploration of sex- and tissue-specific transcriptomic changes with age. In its conception, we developed a novel bioinformatics pipeline leveraging RNA sequencing data, from the GTEx project, encompassing more than 900 individuals. voyAGEr reveals transcriptomic signatures of the known asynchronous ageing between tissues, allowing the observation of tissue-specific age periods of major transcriptional changes, associated with alterations in different biological pathways, cellular composition, and disease conditions. Notably, voyAGEr was created to assist researchers with no expertise in bioinformatics, providing a supportive framework for elaborating, testing and refining their hypotheses on the molecular nature of human ageing and its association with pathologies, thereby also aiding in the discovery of novel therapeutic targets. voyAGEr is freely available at https://compbio.imm.medicina.ulisboa.pt/app/voyAGEr.

An in-depth evaluation of metagenomic classifiers for soil microbiomes

BackgroundRecent endeavours in metagenomics, exemplified by projects such as the human microbiome project and TARA Oceans, have illuminated the complexities of microbial biomes. A robust bioinformatic pipeline and meticulous evaluation of their methodology have contributed to the success of these projects. The soil environment, however, with its unique challenges, requires a specialized methodological exploration to maximize microbial insights. A notable limitation in soil microbiome studies is the dearth of soil-specific reference databases available to classifiers that emulate the complexity of soil communities. There is also a lack of in-vitro mock communities derived from soil strains that can be assessed for taxonomic classification accuracy.ResultsIn this study, we generated a custom in-silico mock community containing microbial genomes commonly observed in the soil microbiome. Using this mock community, we simulated shotgun sequencing data to evaluate the performance of three leading metagenomic classifiers: Kraken2 (supplemented with Bracken, using a custom database derived from GTDB-TK genomes along with its own default database), Kaiju, and MetaPhlAn, utilizing their respective default databases for a robust analysis. Our results highlight the importance of optimizing taxonomic classification parameters, database selection, as well as analysing trimmed reads and contigs. Our study showed that classifiers tailored to the specific taxa present in our samples led to fewer errors compared to broader databases including microbial eukaryotes, protozoa, or human genomes, highlighting the effectiveness of targeted taxonomic classification. Notably, an optimal classifier performance was achieved when applying a relative abundance threshold of 0.001% or 0.005%. The Kraken2 supplemented with bracken, with a custom database demonstrated superior precision, sensitivity, F1 score, and overall sequence classification. Using a custom database, this classifier classified 99% of in-silico reads and 58% of real-world soil shotgun reads, with the latter identifying previously overlooked phyla using a custom database.ConclusionThis study underscores the potential advantages of in-silico methodological optimization in metagenomic analyses, especially when deciphering the complexities of soil microbiomes. We demonstrate that the choice of classifier and database significantly impacts microbial taxonomic profiling. Our findings suggest that employing Kraken2 with Bracken, coupled with a custom database of GTDB-TK genomes and fungal genomes at a relative abundance threshold of 0.001% provides optimal accuracy in soil shotgun metagenome analysis.

VoyAGEr, a free web interface for the analysis of age-related gene expression alterations in human tissues.

We herein introduce voyAGEr, an online graphical interface to explore age-related gene expression alterations in 49 human tissues. voyAGEr offers a visualisation and statistical toolkit for the finding and functional exploration of sex- and tissue-specific transcriptomic changes with age. In its conception, we developed a novel bioinformatics pipeline leveraging RNA sequencing data, from the GTEx project, encompassing more than 900 individuals. voyAGEr reveals transcriptomic signatures of the known asynchronous ageing between tissues, allowing the observation of tissue-specific age periods of major transcriptional changes, associated with alterations in different biological pathways, cellular composition, and disease conditions. Notably, voyAGEr was created to assist researchers with no expertise in bioinformatics, providing a supportive framework for elaborating, testing and refining their hypotheses on the molecular nature of human ageing and its association with pathologies, thereby also aiding in the discovery of novel therapeutic targets. voyAGEr is freely available at https://compbio.imm.medicina.ulisboa.pt/app/voyAGEr.

Development and Validation of a Genotypic Assay to Quantify CXCR4- and CCR5-Tropic Human Immunodeficiency Virus Type-1 (HIV-1) Populations and a Comparison to Trofile®

HIV-1 typically infects cells via the CD4 receptor and CCR5 or CXCR4 co-receptors. Maraviroc is a CCR5-specific viral entry inhibitor; knowledge of viral co-receptor specificity is important prior to usage. We developed and validated an economical V3-env Illumina-based assay to detect and quantify the frequency of viruses utilizing each co-receptor. Plasma from 54 HIV+ participants (subtype B) was tested. The viral template cDNA was generated from plasma RNA with unique molecular identifiers (UMIs). The sequences were aligned and collapsed by the UMIs with a custom bioinformatics pipeline. Co-receptor usage, determined by codon analysis and online phenotype predictors PSSM and Geno2pheno, were compared to existing Trofile® data. The cost of V3-UMI was tallied. The sequences interpreted by Geno2pheno using the most conservative cut-off, a 2% false-positive-rate (FPR), predicted CXCR4 usage with the greatest sensitivity (76%) and specificity (100%); PSSM and codon analysis had similar sensitivity and lower specificity. Discordant Trofile® and genotypic results were more common when participants had specimens from different dates analyzed by either assay. V3-UMI reagents cost USD$62/specimen. A batch of ≤20 specimens required 5 h of technical time across 1.5 days. V3-UMI predicts HIV tropism at a sensitivity and specificity similar to those of Trofile®, is relatively inexpensive, and could be performed by most central laboratories. The adoption of V3-UMI could expand HIV drug therapeutic options in lower-resource settings that currently do not have access to phenotypic HIV tropism testing.

ASAPA: a bioinformatic pipeline based on Iso-Seq that identifies the links among alternative splicing, alternative transcription initiation and alternative polyadenylation.

Although the events associated with alternative splicing (AS), alternative polyadenylation (APA) and alternative transcription initiation (ATI) can be identified by many approaches based on isoform sequencing (Iso-Seq), these analyses are generally independent of each other and the links between these events are still rarely mentioned. However, an interdependency analysis can be achieved because the transcriptional start site, splice sites and polyA site could be simultaneously included in a long, full-length read from Iso-Seq. We create ASAPA pipeline that enables streamlined analysis for a robust detection of potential links among AS, ATI and APA using Iso-Seq data. We tested this pipeline using Arabidopsis data and found some interesting results: some adjacent introns tend to be simultaneously spliced or retained; coupling between AS and ATI or APA is limited to the initial or terminal intron; and ATI and APA are potentially linked in some special cases. Our pipeline enables streamlined analysis for a robust detection of potential links among AS, ATI and APA using Iso-Seq data, which is conducive to a better understanding of transcription landscape generation.