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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2019

In silico analysis of coding SNPs and 3′-UTR associated miRNAs in DCAF17 gene that may affect the regulation and pathogenesis of Woodhouse-Sakati Syndrome

BackgroundWoodhouse-Sakati Syndrome refers to a group of inherited disorders characterized by alopecia, hypogonadism, diabetes mellitus, hypothyroidism and progressive extrapyramidal signs. The aim of this study is to identify the pathogenic SNPs in the DCAF17 gene with their related mciroRNAs and their effect on the structure and function of the protein.\n\nMaterial and MethodsWe used different bioinformatics tools to predict the effect of each SNP on the structure and function of the protein. After that we defined the miRNAs founded in the 3'-UTR region on the DCAF17 gene and studied the annotations relative to it.\n\nResultsTen deleterious SNPs out of 339 were found to have a damaging effect on the protein structure and function, with one significant micoRNA in the 3'-UTR region.\n\nConclusionThis was the first in silico analysis of DCAF17 gene, in which 10 novel mutations were found using different bioinformatics tools that could be used as a diagnostic markers for Woodhouse-Sakati syndrome, with one relevant microRNA that can regulate the function of the protein.

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bioRxiv2019

CRISPR-GNL: an improved model for predicting CRISPR activity by machine learning and featurization

MotivationThe CRISPR/Cas9 system has been broadly used in genetic engineering. However, risks of potential off-targets and the variability of on-target activity among different targets are two limiting factors. Several bioinformatic tools have been developed for CRISPR on-target activity and off-target prediction. However, the general application of the current prediction models is hampered by the great variation among different algorithms.\n\nResultsIn this study, we thoroughly re-analyzed 13 published datasets with eight regression models. We proved that the current model gave very low cross-dataset and cross-species prediction outcome. To overcome these limitations, we have developed an improved model (a generalization score, GNL) based on normalized gene editing activity from 8,101 gRNAs and 2,488 features using Bayesian Ridge Regression model. Our results demonstrated that the GNL model is a better general algorithm for CRISPR on-target activity prediction\n\nAvailability and implementationThe prediction scorer is available on GitHub (https://github.com/TerminatorJ/GNL_Scorer).\n\nContactJ.W. (wangjun6@genomics.cn) or Y.L. (luoyonglun@genomics.cn)\n\nSupplementary InformationSupplementary data are available at Bioinformatics online.

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bioRxiv2019

CAncer bioMarker Prediction Pipeline (CAMPP) - A standardised and user-friendly framework for the analysis of quantitative biological data.

MotivationRecent improvements in -omics and next-generation sequencing (NGS) technologies, and the lowered costs associated with generating these types of data, have made the analysis of high-throughput datasets standard, both for forming and testing biomedical hypotheses. Alongside new wet-lab methodologies, our knowledge of how to normalise bio-data has grown extensively. By removing latent undesirable variances, we obtain standardised datasets, which can be more easily compared between studies. These advancements mean that non-experts in bioinformatics are now faced with the challenge of performing computational data analysis, pre-processing and visualisation. One example could be the analysis of biological data to pinpoint disease-related biomarkers for experimental validation. In this case, bio-researchers will desire an easy and standardised way of analysing high-throughput datasets.\n\nResultsHere we present the CAncer bioMarker Prediction Pipeline (CAMPP), an open-source R-based wrapper intended to aid non-experts in bioinformatics with data analyses. CAMPP is called from a terminal command line and is supported by a user-friendly manual. The pipeline may be run on a local computer and requires little or no knowledge of programming. CAMPP performs missing value imputation and normalisation followed by (I) k-means clustering, (II) differential expression/abundance analysis, (III) elastic-net regression, (IV) correlation and co-expression network analyses, (V) survival analysis and (IV) protein-protein/miRNA-gene interaction networks. The pipeline returns tabular files and graphical representations of the results. We hope that CAMPP will assist biomedical researchers in the analysis of quantitative biological data, whilst ensuring an appropriate biostatistical framework.\n\nAvailability and ImplementationCAMPP is available at https://github.com/ELELAB/CAMPP

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bioRxiv2019

FIGARO: An efficient and objective tool for optimizing microbiome rRNA gene trimming parameters

SummaryMicrobiome studies continue to provide tremendous insight into the importance of microorganism populations to the macroscopic world. High-throughput DNA sequencing technology (i.e., Next-generation Sequencing) has enabled the cost-effective, rapid assessment of microbial populations when combined with bioinformatic tools capable of identifying microbial taxa and calculating the diversity and composition of biological and environmental samples. Ribosomal RNA gene sequencing, where 16S and 18S rRNA gene sequences are used to identify prokaryotic and eukaryotic species, respectively, is one of the most widely-used techniques currently employed in microbiome analysis. Prior to bioinformatic analysis of these sequences, trimming parameters must be set so that post-trimming sequence information is maximized while expected errors in the sequences themselves are minimized. In this application note, we present FIGARO: a Python-based application designed to maximize read retention after trimming and filtering for quality. FIGARO was designed specifically to increase reproducibility and minimize trial-and-error in trimming parameter selection for a DADA2-based pipeline and will likely be useful for optimizing trimming parameters and minimizing sequence errors in other pipelines as well where paired-end overlap is required.\n\nAvailability and implementationThe FIGARO application is freely available as source code at https://github.com/Zymo-Research/figaro.

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bioRxiv2019

Verify Hub Genes of expression profile in aortic dissection

BackgroundTo assess the mRNAs expression profile and explore the hub mRNAs and potential molecular mechanisms in the pathogenesis of human thoracic aortic dissection (TAD). Methodology: mRNA microarray expression signatures of TAD tissues (n=6) and no TAD tissues (NT;n=6) were analyzed by Arraystar human mRNAs microarray. Real-time PCR (qRT-PCR) were used to validate the result of mRNAs microarray. Bioinformatic tools including gene ontology, and Kyoto Encyclopedia of Genes and Genomes pathway analysis were utilized. The protein-protein interaction networks were constructed based on data from the STRING database. Molecular Complex Detection (MCODE) and cytohubba analysis were used to infer the most hug gene and pathways. Results: The top 10 hub genes CDK1, CDC20, CCNB2, CCNB1, MAD2L1, AURKA, C3AR1, NCAPG,CXCL12 and ASPM were identified from the PPI network. Module analysis revealed that TAD was associated with cell cycle, oocyte meiosis, p53 signaling pathway, progesterone-mediated oocyte maturation. The qRT-PCR result showed that the expression of all hug genes was significantly increased in TAD samples (p < 0.05). Conclusions: These candidate genes could be used as potential diagnostic biomarkers and therapeutic targets of TAD.\n\nAuthor summaryMany basic characteristics underlying the establishment of aortic dissection have not been studied in detail. The presented work sought to understand the pathogenesis of human thoracic aortic dissection by employing bioinformatic tools to explore the hub mRNAs and potential molecular mechanisms of thoracic aortic dissection. Many pathway were thought to have relevant with this disease, but the most important pathway was not define. We used bio-mathematical analysis to explore the potential functions in thoracic aortic dissection and identified the hub genes and explored the intrinsic molecular mechanisms involved in thoracic aortic dissection between two microarray analysis. Finally, we indentified the cell cycle maybe the key pathway in thoracic aortic dissection.

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bioRxiv2019

DEN-IM: Dengue Virus identification from shotgun and targeted metagenomics

Dengue virus (DENV) represents a public health and economic burden in affected countries. The availability of genomic data is key to understand viral evolution and dynamics, supporting improved control strategies. Currently, the use of High Throughput Sequencing (HTS) technologies, which can be applied both directly to patient samples (shotgun metagenomics) and to PCR amplified viral sequences (targeted metagenomics), is the most informative approach to monitor the viral dissemination and genetic diversity.\n\nDespite many advantages, these technologies require bioinformatics expertise and appropriate infrastructure for the analysis and interpretation of the resulting data. In addition, the many software solutions available can hamper reproducibility and comparison of results.\n\nHere we present DEN-IM, a one-stop, user-friendly, containerised and reproducible workflow for the analysis of DENV sequencing data, both from shotgun and targeted metagenomics approaches. It is able to infer DENV coding sequence (CDS), identify serotype and genotype, and generate a phylogenetic tree. It can easily be run on any UNIX-like system, from local machines to high-performance computing clusters, performing a comprehensive analysis without the requirement of extensive bioinformatics expertise.\n\nUsing DEN-IM, we successfully analysed two DENV datasets. The first comprised 25 shotgun metagenomic sequencing samples of varying serotype and genotype, including a spiked sample containing the existing four serotypes. The second dataset consisted of 106 targeted metagenomics samples of DENV 3 genotype III where DEN-IM allowed detection of the intra-genotype diversity.\n\nThe DEN-IM workflow, parameters and execution configuration files, and documentation are freely available at https://github.com/B-UMMI/DEN-IM.

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bioRxiv2019

Hermes: an ensemble machine learning architecture for protein secondary structure prediction

MotivationProtein structure predictions, that combine theoretical chemistry and bioinformatics, are an increasingly important technique in biotechnology and biomedical research, for example in the design of novel enzymes and drugs. Here, we present a new ensemble bi-layered machine learning architecture, that directly builds on ten existing pipelines providing rapid, high accuracy, 3-State secondary structure prediction of proteins.\n\nResultsAfter training on 1348 solved protein structures, we evaluated the model with four independent datasets: JPRED4 - compiled by the authors of the successful predictor with the same name, and CASP11, CASP12 & CASP13 - assembled by the Critical Assessment of protein Structure Prediction consortium who run biannual experiments focused on objective testing of predictors. These rigorous, pre-established protocols included 7-fold cross-validation and blind testing. This led to a mean Hermes accuracy of 95.5%, significantly (p<0.05) better than the ten previously published models analysed in this paper. Furthermore, Hermes yielded a reduction in standard deviation, lower boundary outliers, and reduced dependency on solved structures of homologous proteins, as measured by NEFF score. This architecture provides advantages over other pipelines, while remaining accessible to users at any level of bioinformatics experience.\n\nAvailability and ImplementationThe source code for Hermes is freely available at: https://github.com/HermesPrediction/Hermes. This page also includes the cross-validation with corresponding models, and all training/testing data presented in this study with predictions and accuracy.

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bioRxiv2019

AsCRISPR: a web server for allele-specific sgRNA design in precision medicine

Allele-specific genomic targeting by CRISPR provides a point of entry for personalized gene therapy of dominantly inherited diseases, by selectively disrupting the mutant alleles or disease-causing single nucleotide polymorphisms (SNPs), ideally while leaving normal alleles intact. Moreover, the allele-specific engineering has been increasingly exploited not only in treating inherited diseases and mutation-driven cancers, but also in other important fields such as genome imprinting, haploinsufficiency, genome loci imaging and immunocompatible manipulations. Despite the tremendous utilities of allele-specific targeting by CRISPR, very few bioinformatic tools have been implemented for the allele-specific purpose. We thus developed AsCRISPR (Allele-specific CRISPR), a web tool to aid the design of guide RNA (gRNA) sequences that can discriminate between alleles. It provides users with limited bioinformatics skills to analyze both their own identified variants and heterozygous SNPs deposited in the dbSNP database. Multiple CRISPR nucleases and their engineered variants including newly-developed Cas12b and CasX are included for users choice. Meanwhile, AsCRISPR evaluates the on-target efficiencies, specificities and potential off-targets of gRNA candidates, and also displays the allele-specific restriction enzyme sites that might be disrupted upon successful genome edits. In addition, AsCRISPR analyzed with dominant single nucleotide variants (SNVs) retrieved from ClinVar and OMIM databases, and generated a Dominant Database of candidate discriminating gRNAs that may specifically target the alternative allele for each dominant SNV site. A Validated Database was also established, which manually curated the discriminating gRNAs that were experimentally validated in the mounting literatures. AsCRISPR is freely available at http://www.genemed.tech/ascrispr.

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bioRxiv2019

HeveaDB: a genetic resource database for rubber tree genomic study

With the proceedings in the genome study in rubber tree, more and more data were produced. However, these data are still deposited in public databases as raw format, and the utilization of these NGS data is hindered for the researchers in rubber tree community. HeveaDB was therefore constructed and maintained under the organization of International Rubber Research and Development Board. The HeveaDB 1.0 (http://hevea.catas.cn) mainly stores 4 versions of Hevea draft genome, 99 NGS transcriptomes, 18451annotated EST sequences, 12 curated gene families, 30200 gene annotations, 5049 wild germplasm phenotype data, and 18328 Wickham rubber clonal information. Several bioinformatic tools are integrated in the database to facilitate the utilization of the data for the users who are not skilled in bioinformatics. With the periodical update and functional improvement, the HeveaDB will serve as a potential platform for genomic studies and breeding technology in rubber tree.

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bioRxiv2019

Microbiome-wide characterization of post-translational modifications

Studying the structure and function of microbiomes is an emerging research field. Metaproteomic approaches focusing on the characterization of expressed proteins and post-translational modifications (PTMs) provide a deeper understanding of microbial communities. Previous research has highlighted the value of examining microbiome-wide protein expression in studying the roles of the microbiome in human diseases. Nevertheless, the regulation of protein functions in complex microbiomes remains under-explored. This is mainly due to the lack of efficient bioinformatics tools to identify and quantify PTMs in the microbiome. We have developed a comprehensive software termed MetaLab for the data analysis of metaproteomic datasets. Here we build an open search workflow within MetaLab for unbiased identification and quantification of PTMs from microbiome samples. This bioinformatics platform provides information about proteins, PTMs, taxa, functions, and pathways of microbial communities. The performance of the workflow was evaluated using conventional proteomics, metaproteomics from mouse and human gut microbiomes, and modification-specific enriched datasets. Superior accuracy and sensitivity were obtained simultaneously by using our method comparing with the traditional closed search strategy.

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bioRxiv2019

BiomeSeq: A Tool for the Characterization of Animal Microbiomes from Metagenomic Data

The complete characterization of a microbiome is critical in elucidating the complex ecology of the microbial composition within healthy and diseased animals. Many microbiome studies characterize only the bacterial component, for which there are several well-developed sequencing methods, bioinformatics tools and databases available. The lack of comprehensive bioinformatics workflows and databases have limited efforts to characterize the other components existing in a microbiome. BiomeSeq is a tool for the analysis of the complete animal microbiome using metagenomic sequencing data. With its comprehensive workflow, customizable parameters and microbial databases, BiomeSeq can rapidly quantify the viral, fungal, bacteriophage and bacterial components of a sample and produce informative tables for analysis. BiomeSeq was employed in detecting and quantifying the respiratory microbiome of a commercial poultry broiler flock throughout its grow-out cycle from hatching to processing. It successfully processed 780 million reads, of which 5,163 aligned to avian DNA viral genomes, 71,936 aligned to avian RNA viral genomes, 469,937 aligned to bacterial genomes, 504,682 aligned to bacteriophage genomes and 1,964 aligned to fungal genomes. For each microbial species detected, BiomeSeq calculated the normalized abundance, percent relative abundance, and coverage as well as the diversity for each sample. BiomeSeq provides for the detection and quantification of the microbiome from next-generation metagenomic sequencing data. This tool is implemented into a user-friendly container that requires one command and generates a table consisting of taxonomical information for each microbe detected as well as normalized abundance, percent relative abundance, coverage and diversity calculations.

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bioRxiv2019

Zero-shot imputations across species are enabled through joint modeling of human and mouse epigenomics

Recent large-scale efforts to characterize functional activity in human have produced thousands of genome-wide experiments that quantify various forms of biochemistry, such as histone modifications, protein binding, transcription, and chromatin accessibility. Although these experiments represent a small fraction of the possible experiments that could be performed, they also make human more comprehensively characterized than any other species. We propose an extension to the imputation approach Avocado that enables the model to leverage genome alignments and the large number of human genomics data sets when making imputations in other species. We found that not only does this extension result in improved imputation of mouse functional experiments, but that the extended model is able to make accurate imputations for protein binding assays that have been performed in human but not in mouse. This ability to make "zero-shot" imputations greatly increases the utility of such imputation approaches and enables comprehensive imputations to be made for species even when experimental data are sparse. CCS CONCEPTS* Computing methodologies [->] Neural networks; Factorization methods; * Applied computing [->] Bioinformatics; Genomics. ACM Reference FormatJacob Schreiber, Deepthi Hegde, and William Noble. 2020. Zero-shot imputations across species are enabled through joint modeling of human and mouse epigenomics. In ACM-BCB 2020: 11th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, Sept 21-24, 2020, Virtual. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/1122445.1122456

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bioRxiv2019

GAD: a Python script for dividing genome annotation files into feature-based files

Nowadays, manipulating and analyzing publicly available genomic datasets become a daily task in bioinformatics and genomics laboratories. The release of several genome sequencing projects prompts bioinformaticians to develop automated scripts and pipelines which analyze genomic datasets in particular gene annotation pipelines. Handling genome annotation files with fully-featured programs used by non-developers is necessary, furthermore, accelerating genomic data analysis with a focus on diminishing the genome annotation and sequence files based on specific features is required. Consequently, to extract genome features from GTF or GFF3 in a precise manner, GAD script (https://github.com/bio-projects/GAD) provides a simple graphical user interface which interpreted by all python versions installed in different operating systems. GAD script contains unique entry widgets which are capable to analyze multiple genome sequence and annotation files by a click. With highly influential coded functions, genome features such upstream genes, downstream genes, intergenic regions, genes, transcripts, exons, introns, coding sequences, five prime untranslated regions, and three prime untranslated regions and other ambiguous sequence ontology terms will be extracted. GAD script outputs the results in diverse file formats such as BED, GTF/GFF3 and FASTA files which supported by other bioinformatics programs. Our script could be incorporated into various pipelines in all genomics laboratories with the aim of accelerating data analysis.

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bioRxiv2019

Extensive In Silico Analysis of ATL1 Gene: Discovered Five Mutations that may Cause Hereditary Spastic Paraplegia Type 3A

BACKGROUNDHereditary spastic paraplegia type 3A (SPG3A) is a neurodegenerative disease inherited type of Hereditary spastic paraplegia (HSP). It is the second most frequent type of HSP; which Characterized by muscle stiffness with paraplegia and early-onset of symptoms. This is the first translational bioinformatics analysis in a coding region of ATL1 gene which aims to categorize nsSNPs to be used as genomic biomarkers; also it may play a key role in pharmacogenomics by evaluating drug response for this disabling disease.\n\nMETHODSThe raw data of ATL1 gene were retrieved from dbSNP database, and then run into numerous computational analysis tools. Additionally; we submitted the common six deleterious outcomes from the previous functional analysis tools to I-mutant 3.0, and MUPro respectively, to investigate their effect on structural level. The 3D structure of ATL1 was predicted by RaptorX and modeled using UCSF Chimera to compare the differences between the native and the mutant amino acids.\n\nRESULTSFive nsSNPs out of 249 were classified as the most deleterious (rs746927118, rs979765709, rs119476049, rs864622269, rs1242753115).\n\nCONCLUSIONSIn this study the impact of nsSNPs in the ATL1 gene was investigated by various bioinformatics tools, that revealed five nsSNPs (V67F, T120I, R217Q, R495W and G504E) are deleterious SNPs, which have a functional impact on ATL1 protein; and therefore, can be used as genomic biomarkers specifically before 4 years old; also it may play a key role in pharmacogenomics by evaluating drug response for this disabling disease.

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bioRxiv2019

Interlaboratory Data Variability Contributes to the Differential Principal Components of Human Primed and Naïve-like Pluripotent States in Multivariate Meta-Analysis

Currently, genome-wide data analyses have revealed significant differences between various human naive-like pluripotent states derived from different laboratory protocols, confounding the establishment of defining criteria of human naive pluripotency. Thus, it is imperative to understand the concept concerning the ground or naive pluripotent state of pluripotent stem cells, which was initially established in mouse embryonic stem cells (mESCs). Putative human pluripotency has been proposed, largely based on comparing genome-wide transcriptomic signatures of human pluripotent stem cells (hPSCs) with human pre-implantation embryos and mESCs by several research groups. Current bioinformatics approaches, however, have inevitable conceptual biases and technological limitations, including the choices of datasets, analytic methods, and interlaboratory data variability. In this report, we performed a multivariate meta-analysis of major hPSC datasets via the combined analytic powers of percentile normalization, principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and SC3 consensus clustering. This vigorous bioinformatics approach has significantly improved the predictive values of the current meta-analysis. Accordingly, we were able to reveal various fundamental inconsistencies between naive-like hPSCs and their human and mouse in vitro counterparts, which are likely attributed to interlaboratory protocol differences. Moreover, our meta-analysis failed to provide global transcriptomic markers that support the putative in vitro human naive pluripotent state, rather suggesting the existence of altered pluripotent states under current naive-like hPSC growth protocols.

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bioRxiv2019

Insilico Analysis Reveal Three novel nsSNPs May effect on GM2A protein Leading to AB variant of GM2 gangliosidosis

BackgroundAB variant of GM2 gangliosidosis caused as a result of mutations in GM2 activator gene (GM2A) which is regarded as neurodegenerative disorder. This study aimed to predict the possible damaging SNPs of this gene and their impact on the protein using different bioinformatics tools. MethodsSNPs retrieved from the NCBI database were analyzed using several bioinformatics tools. The different tools collectively predicted the effect of single nucleotide substitution on both structure and function of GM2 activator. ResultsThree novel mutations were found to be highly damaging to the structure and function of the GM2A gene. ConclusionFour SNPs were found to be highly damaging SNPs that affect function, structure and stability of GM2A protein, which they are: C99Y, C112F, C138S and C138R, three of them are novel SNPs (C99Y, C112F and C138S). Also 46 SNPs were predicted to affect miRNAs binding sites on 3UTR leading to abnormal expression of the resulting protein. These SNPs should be considered as important candidates in causing AB variant of GM2 gangliosidosis and may help in diagnosis and genetic screening of the disease.

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bioRxiv2025

AEGIS: an annotation extraction and genomic integration resource

The GTF/GFF3 formats are the standard for storing and exchanging genome annotations. However, their flexibility often results in inconsistent and poorly formatted files across different sources, creating a major bottleneck for downstream bioinformatics analyses. Here, we present Annotation Extraction and Genomic Integration Suite (AEGIS), a comprehensive and user-friendly command-line toolkit designed to parse, validate and standardise genome annotation files. AEGIS robustly corrects common structural and formatting errors, ensuring interoperability with downstream tools. Beyond standardisation, the suite provides advanced modules for analysis, such as flexible sequence extraction (e.g. genes, CDS, proteins) with isoform handling, customisable promoter region definitions and targeted DNA motif searches. A key feature of AEGIS is its integrated workflow for comparative genomics, which combines multiple lines of evidence (i.e., sequence homology, synteny and coordinate-based lift-overs) to enable a robust gene ID correspondence and orthology assessment. We demonstrate the utility of AEGIS by comparing two major Arabidopsis thaliana annotations (TAIR10 vs. Araport11), successfully identifying and quantifying complex structural changes such as gene splits and fusions. AEGIS provides a unified solution for annotation quality control, feature extraction and comparative genomic analysis, simplifying complex workflows and enhancing reliability in bioinformatic research. The software is open-source, implemented in Python and is available on GitHub, PyPI, and as a Docker container to ensure accessibility and reproducibility.

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bioRxiv2025

Unified down-stream analysis of crosslinking mass spectrometry results with pyXLMS

Crosslinking mass spectrometry has become the method of choice for the identification of protein-protein interactions and for gaining insight into the structures of proteins in vivo. However, connecting crosslink search engine results with down-stream analysis tools, and therefore gaining biological insight from crosslink identifications, has remained a manual and cumbersome step in the analysis that often requires expert bioinformatics knowledge. Here we introduce pyXLMS, a python package and public web application which aims to simplify and streamline this intermediate step, enabling researchers even without bioinformatics knowledge to conduct in-depth crosslink analyses. In its current state pyXLMS supports input from seven different crosslink search engines, as well as the mzI-dentML format of the HUPO Proteomics Standards Initiative. Down-stream analysis is facilitated by functionality that is directly available within pyXLMS such as aggregation, validation, annotation, filtering, and visualization. In addition, the data can easily be exported to more than ten supported down-stream analysis tools and formats. We demonstrate the applicability and benefits of pyXLMS by re-analyzing a publicly available crosslink dataset with a variety of different search engines and show how the same data analysis workflow can be applied using pyXLMS. pyXLMs is available via https://github.com/hgb-bin-proteomics/pyXLMS.

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