Top 10 Biorxiv Papers Today in Bioinformatics


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#1. New candidates for regulated gene integrity revealed through precise mapping of integrative genetic elements
Catherine M Mageeney, Britney Y Lau, Julian M Wagner, Corey M Hudson, Joseph S Schoeniger, Raga Krishnakumar, Kelly P Williams
Integrative genetic elements (IGEs) are mobile multigene DNA units that integrate into and excise from host bacterial chromosomes. Each IGE usually targets a specific site within a conserved host gene, integrating in a manner that preserves target gene function. However, a small number of bacterial genes are known to be inactivated upon IGE integration and reactivated upon excision, regulating phenotypes of virulence, mutation rate, and terminal differentiation in multicellular bacteria. The list of regulated gene integrity (RGI) cases has been slow-growing because IGEs have been challenging to precisely and comprehensively locate in genomes. We present software (TIGER) that maps IGEs with unprecedented precision and without attB site bias. TIGER uses a comparative genomic, ping-pong BLAST approach, based on the principle that the IGE integration module (i.e., its int-attP region) is cohesive. The resultant IGEs, along with integrase phylogenetic analysis and gene inactivation tests, revealed 19 new cases of genes whose integrity...
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#2. Pathway Mining and Data Mining in Functional Genomics. An Integrative Approach to Delineate Boolean Relationships Between Src and Its targets
Mehran Piran, Pedro L Fernandes, Neda Sepahi, Mehrdad Piran, Amir Rahimi
In recent years the volume of biological data has soared. Parallel to this growth, the need for developing data mining strategies has not met sufficiently. Bioinformaticians use different techniques of data mining to obtain the required information they need from genomic, transcriptomic and proteomic databases. One of the simple mining approaches to construct a gene regulatory network (GNR) is reading a great deal of papers to configure a large network (for instance, a network with 50 nodes and 200 edges) which takes a lot of time and energy. Here we introduce an integrative method that combines information from transcriptomic data with sets of constructed pathways. A program was written in R that makes pathways from edgelists in different signaling databases. Furthermore, we explain how to distinguish false pathways from the correct ones using literature study or incorporating the biological information with gene expression results. This approach can help bioinformaticians and mathematical biologists who work with GRNs when they...
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#3. AITL: Adversarial Inductive Transfer Learning with input and output space adaptation for pharmacogenomics
Hossein Sharifi Noghabi, Shuman Peng, Olga Zolotareva, Colin C Collins, Martin Ester
Motivation: the goal of pharmacogenomics is to predict drug response in patients using their single- or multi-omics data. A major challenge is that clinical data (i.e. patients) with drug response outcome is very limited, creating a need for transfer learning to bridge the gap between large pre-clinical pharmacogenomics datasets (e.g. cancer cell lines), as a source domain, and clinical datasets as a target domain. Two major discrepancies exist between pre-clinical and clinical datasets: 1) in the input space, the gene expression data due to difference in the basic biology, and 2) in the output space, the different measures of the drug response. Therefore, training a computational model on cell lines and testing it on patients violates the i.i.d assumption that train and test data are from the same distribution. Results: We propose Adversarial Inductive Transfer Learning (AITL), a deep neural network method for addressing discrepancies in input and output space between the pre-clinical and clinical datasets. AITL takes gene...
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#4. Comparison of visualisation tools for single-cell RNAseq data
Batuhan Cakir, Martin Prete, Ni Huang, Stijn van Dongen, Pnar Pir, Vladimir Yu. Kiselev
In the last decade, single cell RNAseq (scRNAseq) datasets have grown from a single cell to millions of cells. Due to its high dimensionality, the scRNAseq data contains a lot of valuable information, however, it is not always feasible to visualise and share it in a scientific report or an article publication format. Recently, a lot of interactive analysis and visualisation tools have been developed to address this issue and facilitate knowledge transfer in the scientific community. In this study, we review and compare several of the currently available analysis and visualisation tools and benchmark those that allow to visualize the scRNAseq data on the web and share it with others. To address the problem of format compatibility for most visualisation tools, we have also developed a user-friendly R package, sceasy, which allows users to convert their own scRNAseq datasets into a specific data format for visualisation.
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biorxivpreprint: Comparison of visualisation tools for single-cell RNAseq data https://t.co/X6gvL8bTLL #bioRxiv
biorxiv_bioinfo: Comparison of visualisation tools for single-cell RNAseq data https://t.co/t6LIYqGZGq #biorxiv_bioinfo
OmicsOmicsBlog: RT @biorxivpreprint: Comparison of visualisation tools for single-cell RNAseq data https://t.co/X6gvL8bTLL #bioRxiv
epigeneticsHMGU: RT @biorxivpreprint: Comparison of visualisation tools for single-cell RNAseq data https://t.co/X6gvL8bTLL #bioRxiv
Astro_Erik: RT @biorxivpreprint: Comparison of visualisation tools for single-cell RNAseq data https://t.co/X6gvL8bTLL #bioRxiv
EdelOToole: RT @biorxivpreprint: Comparison of visualisation tools for single-cell RNAseq data https://t.co/X6gvL8bTLL #bioRxiv
GenomeRIK: RT @biorxivpreprint: Comparison of visualisation tools for single-cell RNAseq data https://t.co/X6gvL8bTLL #bioRxiv
cartalop: RT @biorxivpreprint: Comparison of visualisation tools for single-cell RNAseq data https://t.co/X6gvL8bTLL #bioRxiv
David_McGaughey: RT @biorxiv_bioinfo: Comparison of visualisation tools for single-cell RNAseq data https://t.co/t6LIYqGZGq #biorxiv_bioinfo
bdean_: RT @biorxivpreprint: Comparison of visualisation tools for single-cell RNAseq data https://t.co/X6gvL8bTLL #bioRxiv
SaubashyaSur: RT @biorxivpreprint: Comparison of visualisation tools for single-cell RNAseq data https://t.co/X6gvL8bTLL #bioRxiv
hrhotz: RT @biorxiv_bioinfo: Comparison of visualisation tools for single-cell RNAseq data https://t.co/t6LIYqGZGq #biorxiv_bioinfo
DSL79566674: RT @biorxivpreprint: Comparison of visualisation tools for single-cell RNAseq data https://t.co/X6gvL8bTLL #bioRxiv
abhimans: RT @biorxivpreprint: Comparison of visualisation tools for single-cell RNAseq data https://t.co/X6gvL8bTLL #bioRxiv
bohdanome: RT @biorxiv_bioinfo: Comparison of visualisation tools for single-cell RNAseq data https://t.co/t6LIYqGZGq #biorxiv_bioinfo
dalilapinto_sci: RT @biorxivpreprint: Comparison of visualisation tools for single-cell RNAseq data https://t.co/X6gvL8bTLL #bioRxiv
AyalaTovy: RT @biorxivpreprint: Comparison of visualisation tools for single-cell RNAseq data https://t.co/X6gvL8bTLL #bioRxiv
BadFX75: RT @biorxivpreprint: Comparison of visualisation tools for single-cell RNAseq data https://t.co/X6gvL8bTLL #bioRxiv
NZthRzXeiplAQA6: RT @biorxiv_bioinfo: Comparison of visualisation tools for single-cell RNAseq data https://t.co/t6LIYqGZGq #biorxiv_bioinfo
swapnilsbawage: RT @biorxivpreprint: Comparison of visualisation tools for single-cell RNAseq data https://t.co/X6gvL8bTLL #bioRxiv
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#5. Tumor Neoantigenicity Assessment with CSiN Score Incorporates Clonality and Immunogenicity to Predict Immunotherapy Outcomes
Tianshi Lu, Shidan Wang, Lin Xu, Qinbo Zhou, Nirmish Singla, Jianjun Gao, Subrata Manna, Laurentiu Pop, Zhiqun Xie, Mingyi Chen, Jason J Luke, James Brugarolas, Raquibul Hannan, Tao Wang
Lack of responsiveness to checkpoint inhibitors is a central problem in the modern era of cancer immunotherapy. Tumor neoantigens are critical mediators of host immune response and immunotherapy treatment efficacy. Current studies of neoantigens almost entirely focus on total neoantigen load, which simplistically treats all neoantigens equally. Besides, neoantigen loads have been linked with treatment response and prognosis only in some studies, but not others. We developed a Cauchy-Schwarz index of Neoantigens (CSiN) score to characterize the degree of concentration of immunogenic neoantigens in truncal mutations. Unlike simple neoantigen loads, CSiN incorporates the effect of both clonality and MHC-binding affinity of neoantigens when characterizing patient neoantigen profiles. By exploiting the clinical responses in 501 treated patients (mostly by checkpoint inhibitors) and the overall survival of 1,978 baseline patients, we showed that CSiN scores predict treatment response to checkpoint inhibitors and prognosis in melanoma,...
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#6. The 2019-new Coronavirus epidemic: evidence for virus evolution
Domenico Benvenuto, Marta Giovanetti, Alessandra Ciccozzi, Silvia Spoto, Silvia Angeletti, Massimo Ciccozzi
There is concern about a new coronavirus, the 2019-nCoV, as a global public health threat. In this article, we provide a preliminary evolutionary and molecular epidemiological analysis of this new virus. A phylogenetic tree has been built using the 15 available whole genome sequence of 2019-nCoV and 12 whole genome sequences highly similar sequences available in gene bank (5 from SARS, 2 from MERS and 5 from Bat SARS-like Coronavirus). FUBAR analysis shows that the Nucleocapsid and the Spike Glycoprotein has some sites under positive pressure while homology modelling helped to explain some molecular and structural differences between the viruses. The phylogenetic tree showed that 2019.nCoV significantly clustered with Bat SARS-like Coronavirus sequence isolated in 2015, whereas structural analysis revealed mutation in S and nucleocapsid proteins. From these results, 2019nCoV could be considered a coronavirus distinct from SARS virus, probably transmitted from bats or another host where mutations conferred upon it the ability to...
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biorxivpreprint: The 2019-new Coronavirus epidemic: evidence for virus evolution https://t.co/TXfDxkIFgi #bioRxiv
biorxiv_bioinfo: The 2019-new Coronavirus epidemic: evidence for virus evolution https://t.co/Tn3KRw2pVM #biorxiv_bioinfo
km_tsui: RT @biorxivpreprint: The 2019-new Coronavirus epidemic: evidence for virus evolution https://t.co/TXfDxkIFgi #bioRxiv
DrStagiaire: RT @biorxiv_bioinfo: The 2019-new Coronavirus epidemic: evidence for virus evolution https://t.co/Tn3KRw2pVM #biorxiv_bioinfo
MaFerzta: RT @biorxiv_bioinfo: The 2019-new Coronavirus epidemic: evidence for virus evolution https://t.co/Tn3KRw2pVM #biorxiv_bioinfo
jens_uwe_ulrich: RT @biorxiv_bioinfo: The 2019-new Coronavirus epidemic: evidence for virus evolution https://t.co/Tn3KRw2pVM #biorxiv_bioinfo
actgist: RT @biorxivpreprint: The 2019-new Coronavirus epidemic: evidence for virus evolution https://t.co/TXfDxkIFgi #bioRxiv
sagaplague: RT @biorxivpreprint: The 2019-new Coronavirus epidemic: evidence for virus evolution https://t.co/TXfDxkIFgi #bioRxiv
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#7. Extending rnaSPAdes functionality for hybrid transcriptome assembly
Andrey D. Prjibelski, Giuseppe D. Puglia, Dmitry Antipov, Elena Bushmanova, Daniela Giordano, Alla Mikheenko, Domenico Vitale, Alla Lapidus
Background: De novo RNA-Seq assembly is a powerful method for analysing transcriptomes when the reference genome is not available or poorly annotated. However, due to the short length of Illumina reads it is usually impossible to reconstruct complete sequences of complex genes and alternative isoforms. Recently emerged possibility to generate long RNA reads, such as PacBio and Oxford Nanopores, may dramatically improve the assembly quality, and thus the consecutive analysis. While reference-based tools for analysing long RNA reads were recently developed, there is no established pipeline for de novo assembly of such data. Results: In this work we present a novel method that allows to perform high-quality de novo transcriptome assemblies by combining accuracy and reliability of short reads with exon structure information carried out from long error-prone reads. The algorithm is designed by incorporating existing hybridSPAdes approach into rnaSPAdes pipeline and adapting it for transcriptomic data. Conclusion: To evaluate the...
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evol_genomics: RT @biorxiv_bioinfo: Extending rnaSPAdes functionality for hybridtranscriptome assembly https://t.co/vfCyBxmTjK #biorxiv_bioinfo
deepakkumar3san: RT @biorxiv_bioinfo: Extending rnaSPAdes functionality for hybridtranscriptome assembly https://t.co/vfCyBxmTjK #biorxiv_bioinfo
katatonikkat: RT @biorxiv_bioinfo: Extending rnaSPAdes functionality for hybridtranscriptome assembly https://t.co/vfCyBxmTjK #biorxiv_bioinfo
mazepago: RT @biorxivpreprint: Extending rnaSPAdes functionality for hybridtranscriptome assembly https://t.co/JbjzcOxPl0 #bioRxiv
LokeshBio: RT @biorxiv_bioinfo: Extending rnaSPAdes functionality for hybridtranscriptome assembly https://t.co/vfCyBxmTjK #biorxiv_bioinfo
MaFerzta: RT @biorxiv_bioinfo: Extending rnaSPAdes functionality for hybridtranscriptome assembly https://t.co/vfCyBxmTjK #biorxiv_bioinfo
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#8. Genomic alterations and abnormal expression of APE2 in multiple cancers
Katherine A. Jensen, Xinghua Shi, Shan Yan
Although APE2 plays essential roles in base excision repair and ATR-Chk1 DNA damage response (DDR) pathways, it remains unknown how the APE2 gene is altered in the human genome and whether APE2 is differentially expressed in cancer patients. Here, we report multiple-cancer analyses of APE2 genomic alterations and mRNA expression from cancer patients using available data from The Cancer Genome Atlas (TCGA). We observe that APE2 genomic alterations occur at ~17% frequency in 14 cancer types (n= 21,769). Most frequent somatic mutations of APE2 appear in uterus (2.89%) and skin (2.47%) tumor samples. Furthermore, APE2 expression is upregulated in tumor tissue compared with matched non-malignant tissue across 5 cancer types including kidney, breast, lung, liver, and uterine cancers, but not in prostate cancer. We also examine the mRNA expression of 13 other DNA repair and DDR genes from matched samples for 6 cancer types. We show that APE2 mRNA expression is positively correlated with PCNA, APE1, XRCC1, PARP1, Chk1, and Chk2 across...
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biorxivpreprint: Genomic alterations and abnormal expression of APE2 in multiple cancers https://t.co/K2GMot1wzf #bioRxiv
biorxiv_bioinfo: Genomic alterations and abnormal expression of APE2 in multiple cancers https://t.co/dgg7kjPw6P #biorxiv_bioinfo
In_AnkitSingla: RT @biorxivpreprint: Genomic alterations and abnormal expression of APE2 in multiple cancers https://t.co/K2GMot1wzf #bioRxiv
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#9. Single-cell RNA expression profiling of ACE2, the putative receptor of Wuhan 2019-nCov
Yu Zhao, Zixian Zhao, Yujia Wang, Yueqing Zhou, Yu Ma, Wei Zuo
A novel coronavirus (2019-nCov) was identified in Wuhan, Hubei Province, China in December of 2019. This new coronavirus has resulted in thousands of cases of lethal disease in China, with additional patients being identified in a rapidly growing number internationally. 2019-nCov was reported to share the same receptor, Angiotensin-converting enzyme 2 (ACE2), with SARS-Cov. Here based on the public database and the state-of-the-art single-cell RNA-Seq technique, we analyzed the ACE2 RNA expression profile in the normal human lungs. The result indicates that the ACE2 virus receptor expression is concentrated in a small population of type II alveolar cells (AT2). Surprisingly, we found that this population of ACE2-expressing AT2 also highly expressed many other genes that positively regulating viral reproduction and transmission. A comparison between eight individual samples demonstrated that the Asian male one has an extremely large number of ACE2-expressing cells in the lung. This study provides a biological background for the...
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DSL79566674: RT @biorxivpreprint: Single-cell RNA expression profiling of ACE2, the putative receptor of Wuhan 2019-nCov https://t.co/SvvY1S355q #bioRx…
Nandox_85: RT @biorxiv_bioinfo: Single-cell RNA expression profiling of ACE2, the putative receptor of Wuhan 2019-nCov https://t.co/JydaooFYRj #biorx…
RNA_unfolder: RT @biorxivpreprint: Single-cell RNA expression profiling of ACE2, the putative receptor of Wuhan 2019-nCov https://t.co/SvvY1S355q #bioRx…
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#10. Transcription factor enrichment analysis (TFEA): Quantifying the activity of hundreds of transcription factors from a single experiment
Jonathan D. Rubin, Jacob T. Stanley, Rutendo F. Sigauke, Cecilia B. Levandowski, Zachary L. Maas, Jessica Westfall, Dylan J. Taatjes, Robin D. Dowell
Detecting differential activation of transcription factors (TFs) in response to perturbation provides insight into cellular processes. Transcription Factor Enrichment Analysis (TFEA) is a robust and reliable computational method that detects differential activity of hundreds of TFs given any set of perturbation data. TFEA draws inspiration from GSEA and detects positional motif enrichment within a list of ranked regions of interest (ROIs). As ROIs are typically inferred from the data, we also introduce muMerge, a statistically principled method of generating a consensus list of ROIs from multiple replicates and conditions. TFEA is broadly applicable to data that informs on transcriptional regulation including nascent (eg. PRO-Seq), CAGE, ChIP-Seq, and accessibility (e.g. ATAC-Seq). TFEA not only identifies the key regulators responding to a perturbation, but also temporally unravels regulatory networks with time series data. Consequently, TFEA serves as a hypothesis-generating tool that provides an easy, rigorous, and...
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biorxivpreprint: Transcription factor enrichment analysis (TFEA): Quantifying the activity of hundreds of transcription factors from a single experiment https://t.co/lraqN5JfyO #bioRxiv
biorxiv_bioinfo: Transcription factor enrichment analysis (TFEA): Quantifying the activity of hundreds of transcription factors from a single experiment https://t.co/7zz5vfDcpQ #biorxiv_bioinfo
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