PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.27.354092v1?rss=1

Authors: Sowunmi, K., Soyebo, T. A., Okosesi, E. A., Adesiyan, A. L., Oladimeji, K. A., Ajibola, O. A., Ogunlana, Y. O., Agboola, O. W., Kaur, G., Atoromola, H., Oladipupo, T. A.

Abstract:

The development of precise DNA editing nucleases that induce double-strand breaks (DSBs) - including zinc finger nucleases, TALENs, and CRISPR/Cas systems - has revolutionized gene editing and genome engineering. Endogenous DNA DSB repair mechanisms are often leveraged to enhance editing efficiency and precision. While the non-homologous end joining (NHEJ) and homologous recombination (HR) DNA DSB repair pathways have already been the topic of an excellent deal of investigation, an alternate pathway, microhomology-mediated end joining (MMEJ), remains relatively unexplored. However, the MMEJ pathway's ability to supply reproducible and efficient deletions within the course of repair makes it a perfect pathway to be used in gene knockouts. (Microhomology Evoked Deletion Judication EluciDation) may be a random forest machine learning-based method for predicting the extent to which the location of a targeted DNA DSB are going to be repaired using the MMEJ repair pathway. On an independent test set of 24 HeLa cell DSB sites, MEDJED achieved a Pearson coefficient of correlation (PCC) of 81.36%, Mean Absolute Error (MAE) of 10.96%, and Root Mean Square Error (RMSE) of13.09%. This performance demonstrates MEDJED's value as a tool for researchers who wish to leverage MMEJ to supply efficient and precise gene knock outs.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.27.356766v1?rss=1

Authors: Hess, J. L., Chen, S., Quinn, T. P., Neuropsychiatric Consortium for Analysis and Sharing of Transcriptomes,, Kong, S. W., Cairns, M., Tsuang, M., Faraone, S. V., Glatt, S. J.

Abstract:

Ex vivo molecular analysis of the human brain is virtually impossible given major risks and ethical concerns. Transcriptome imputation offers a promising and non-invasive alternative for developing models (albeit imperfect) of brain gene expression in lieu of biopsying brain tissue. Popular tools such as FUSION (Gusev et al., 2016) and PrediXcan (Gamazon et al., 2015) use genotypes at common cis-expression quantitative trait loci (eQTLs) to predict tissue-specific gene expression levels. However, those tools cannot reliably predict expression levels for a majority of genes in the brain. This raises the question of whether an alternative modeling approach should be evaluated to capture greater variance in more genes in the brain that are not yet imputable with existing cis-eQTL imputation tools. To address this problem, we developed a novel transcriptome-imputation method called the Brain Gene Expression and Network Imputation Engine (BrainGENIE) that imputes brain-region-specific gene expression levels from peripheral blood gene expression. BrainGENIE predicted brain-region-specific expression levels for 1,733 - 11,569 genes (cross-validation R2[≥]0.01, false-discovery rate-adjusted p<0.05), few of which are imputable by PrediXcan. Disease-related transcriptome signals detected by BrainGENIE showed stronger agreement with known transcriptome signatures from postmortem brain when compared with findings from analyses of peripheral blood or S-PrediXcan. BrainGENIE complements and outperforms existing transcriptome-imputation tools, provides biologically meaningful predictions, and opens avenues for studying brain transcriptomes longitudinally. BrainGENIE was developed using R (v.3.6.3, tested in 4.0.2) and is freely available at: https://github.com/hessJ/BrainGENIE.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.26.356287v1?rss=1

Authors: Flynn, E., Chang, A., Altman, R. B.

Abstract:

Women are at more than 1.5-fold higher risk for clinically relevant adverse drug events. While this higher prevalence is partially due to gender-related effects, biological sex differences likely also impact drug response. Publicly available gene expression databases provide a unique opportunity for examining drug response at a cellular level. However, missingness and heterogeneity of metadata prevent large-scale identification of drug exposure studies and limit assessments of sex bias. To address this, we trained organism-specific models to infer sample sex from gene expression data, and used entity normalization to map metadata cell line and drug mentions to existing ontologies. Using this method, we infer sex labels for 450,371 human and 245,107 mouse microarray and RNA-seq samples from refine.bio. Overall, we find slight female bias (52.1%) in human samples and (62.5%) male bias in mouse samples; this corresponds to a majority of single sex studies, split between female-only and male-only (33.3% vs 18.4% in human and 31.0% vs 30.4% in mouse respectively). In drug studies, we find limited evidence for sex-sampling bias overall; however, specific categories of drugs, including human cancer and mouse nervous system drugs, are enriched in female-only and male-only studies respectively. Our expression-based sex labels allow us to further examine the complexity of cell line sex and assess the frequency of metadata sex label misannotations (2-5%). We make our inferred and normalized labels, along with flags for misannotated samples, publicly available to catalyze the routine use of sex as a study variable in future analyses.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.26.355941v1?rss=1

Authors: Yang, G., Sabunciyan, S., Florea, L. D.

Abstract:

Alternative splicing of mRNA is an essential gene regulatory mechanism with important roles in development and disease. We present MntJULiP, a method for comprehensive and accurate quantification of splicing differences between two or more conditions. MntJULiP implements novel Dirichlet-multinomial and zero-inflated negative binomial models within a Bayesian framework to detect both changes in splicing ratios and in absolute splicing levels of introns with high accuracy, and can find classes of variation overlooked by reference tools. Additionally, a mixture model allows multiple conditions to be compared simultaneously. Highly scalable, it processed hundreds of GTEx samples in <1 hour to reveal splicing constituents of tissue differentiation.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.26.356204v1?rss=1

Authors: Xue, G., Wang, G., Shi, Q., Wang, H., Lv, B.-M., Gao, M., Niu, X., Zhang, H.-Y.

Abstract:

Achieving an improved understanding of the temporal sequence of factors involved in Parkinson's disease (PD) pathogenesis may accelerate drug discovery. In this study, we performed a longitudinal transcriptome analysis to identify associated genes underlying the pathogenesis of PD at three temporal phases. We firstly found that multiple initiator genes, which are related to processes of olfactory transduction and stem cell pluripotency, indicate PD risk to those subjects at the prodromal phase. And many facilitator genes involved in calcium signaling and stem cell pluripotency contribute to PD onset. We next identified 325 aggravator genes whose expression could lead to disease progression through damage to dopaminergic synapses and ferroptosis via an integrative analysis with DNA methylation. Last, we made a systematic comparison of gene expression patterns across PD development and accordingly provided candidate drugs at different phases in an attempt to prevent the neurodegeneration process.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.26.354357v1?rss=1

Authors: Ramos, T., Galindo, N., Arias-Carrasco, R., da Silva, C., Maracaja-Coutinho, V., do Rego, T.

Abstract:

Non-coding RNAs (ncRNAs) are important players in the cellular regulation of organisms from different kingdoms. One of the key steps in ncRNAs research is the ability to distinguish coding/non-coding sequences. We applied 7 machine learning algorithms (Naive Bayes, SVM, KNN, Random Forest, XGBoost, ANN and DL) through 15 model organisms from different evolutionary branches. Then, we created a stand-alone and web server tool (RNAmining) to distinguish coding and non-coding sequences, selecting the algorithm with the best performance (XGBoost). Firstly, we used coding/non-coding sequences downloaded from Ensembl (April 14th, 2020). Then, coding/non-coding sequences were balanced, had their tri-nucleotides counts analysed and we performed a normalization by the sequence length. Thus, in total we built 180 models. All the machine learning algorithms tests were performed using 10-folds cross-validation and we selected the algorithm with the best results (XGBoost) to implement at RNAmining. Best F1-scores ranged from 97.56% to 99.57% depending on the organism. Moreover, we produced a benchmarking with other tools already in literature (CPAT, CPC2, RNAcon and Transdecoder) and our results outperformed them, opening opportunities for the development of RNAmining, which is freely available at https://rnamining.integrativebioinformatics.me/ .

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.26.354621v1?rss=1

Authors: Xu, M., Guo, L., Shi, C., Liu, X., Chen, J., Liu, X., Fan, G.

Abstract:

Decontamination is necessary for eliminating the effect of foreign genomes on the symbiont studies and biomedical discoveries. However, direct extraction of host sequencing reads with no references remains challenging. Here, we present a trio-based method to classify the host error-prone long reads or sparse co-barcoded reads prior to assembly, free of any alignments against DNA or protein references. This method first identifies high-confident host reads by haplotype-specific k-mers inherited from parents, and then groups remaining host reads by the unsupervised clustering. Experimental results demonstrated that this approach successfully classified up to 97.38% of the host human long reads with the precision rate of 99.9999%, and 79.95% host co-barcoded reads with the precision rate of 98.36% using an artificially mixed data. Moreover, the tool also exhibited a good performance on the decontamination of the real algae data. The purified reads reconstructed two haplotypes and improved the assembly with larger contig NGA50 value and less misassemblies. Symbiont-Screener can be freely downloaded at https://github.com/BGI-Qingdao/Symbiont-Screener.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.26.355180v1?rss=1

Authors: Ostvar, S., Troung, H., Silver, E. R., Lightdale, C. J., Hur, C., Tatonetti, N. P.

Abstract:

Esophageal adenocarcinoma (EAC) is a rare but lethal cancer with rising incidence in several global hotspots including the United States. The five-year survival rate for patients diagnosed with advanced disease can be as low as 5% in EAC, making early detection and preventive intervention crucial. The current standard of care for EAC targets patients with Barrett's esophagus (BE), the main precursor to EAC and a relatively common condition in adults with chronic acid reflux disease. Preventive care for EAC requires repeated surveillance endoscopies of BE patients with biopsy sampling, and can be intrusive, error-prone, and costly. The integration of minimally-invasive subsurface tissue imaging in the current standard of care can reduce the need for exhaustive tissue sampling and improve the quality of life in BE patients. Effective adoption of subsurface imaging in EAC care can be facilitated by computer-aided detection (CAD) systems based on deep learning. Despite their recent successes in lung and breast cancer imaging, the development of deep neural networks for rare conditions like EAC remains challenging due to data scarcity, heavy bias in existing datasets toward non-cases, and uncertainty in image labels. Here we explore the use of synthetic datasets--specifically data derived from simulations of optical back-scattering during imaging-- in the development of CAD systems based on deep learning. As a proof of concept, we studied the binary classification of esophageal OCT into normal squamous and glandular mucosae, typical of BE. We found that deep convolutional networks trained on synthetic data had improved performance over models trained on clinical datasets with uncertain labels. Model performance also improved with dataset size during training on synthetic data. Our findings demonstrate the utility of transfer from simulations to real data in the context of medical imaging, especially in the severely data-poor regime and when significant uncertainty in labels are present, and motivate further development of transfer learning from simulations to aid the development of CAD for rare malignancies.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.26.351783v1?rss=1

Authors: Badam, T. V. S., de Weerd, H. A., Martinez-Enguita, D., Olsson, T., Alfredsson, L., Kockum, I., Jagodic, M., Lubovac-Pilav, Z., Gustafsson, M.

Abstract:

Disease modules are popular concepts for interpreting genome-wide studies in medicine but have so far not been methodically evaluated, and no guidelines for predictive multi-omics integration exist. We assessed eight module identification methods in 57 expression and methylation studies of 19 diseases using GWAS enrichment analysis, which showed modules inferred from clique-based methods being most enriched. Next, we applied the same strategy for multi-omics integration of 19 datasets of multiple sclerosis (MS), which allowed the robust identification of a module of 220 genes. Most genes of the module could be epigenetically validated (n = 217, P = 10-47) and were also independently validated for association with five different risk factors of MS, which further stressed the high relevance of the module for MS. We believe our analysis provides a workflow for improving disease module methods, but also for combining and assessing the performance of multi-omics approaches for complex diseases.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.26.355222v1?rss=1

Authors: Loh, B., Chen, J., Manohar, P., Yu, Y., Hua, X., Leptihn, S.

Abstract:

Acinetobacter baumannii is of major clinical importance as the bacterial pathogen often causes hospital acquired infections, further complicated by the high prevalence of antibiotic resistant strains. Aside from natural tolerance to certain antibiotic classes, resistance is often acquired by the exchange of genetic information via conjugation but also by the high natural competence exhibited by A. baumannii. In addition, bacteriophages are able to introduce resistance genes but also toxins and virulence factors via phage mediated transduction. In this work, we analysed the complete genomes of 177 A. baumannii strains for the occurrence of prophages, and analysed their taxonomy, size and positions of insertion. Among all the prophages that were detected, Siphoviridae and Myoviridae were the two most commonly found families, while the average genome size was determined as 3.98 Mbp. Our data shows the wide variation in the number of prophages in A. baumannii genomes and the prevalence of certain prophages within strains that are most successful or potentially beneficial to the host. Our study also revealed that only two specific sites of insertion within the genome of the host bacterium are being used, with few exceptions only. Lastly, we analysed the existence of genes that are encoded in the prophages, which confer antimicrobial resistance (AMR). Several phages carry AMR genes, including OXA-23 and NDM-1, illustrating the importance of lysogenic phages in the acquisition of resistance genes.

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