PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.15.285551v1?rss=1

Authors: Ali, A., Shehwana, H., Hanif, A., Fatima, A., Shabbir, M., Rafiq, M.

Abstract:

Sepsis is a serious health situation caused by uncontrolled infection and septic shock is a severe condition of sepsis. RHBDD2 is a member of the rhomboid superfamily which is overexpressed in different types of cancer and associated with ER stress and estrogen receptor. Using microarray gene expression data and using different computational techniques this study investigated the role of RHBDD2 in sepsis and septic shock. Finds functional annotation of RHBDD2 using co-expression analysis and identified the deregulation of RHBDD2 in sepsis using differential expression analysis. Results show that RHBDD2 is overexpressed in sepsis and septic shock. The GO enrichment analysis, KEGG pathways, and biological functions of the RHBDD2 co-expressed genes module show that it is involved in most of the sepsis-related biological functions and also plays a role in most of the infection-related pathways which lead to sepsis and septic shock. RHBDD2 is regulated by STAT5 and SP1 transcriptional factors in sepsis and septic shock. The identification of the RHBDD2 as a biomarker may facilitate in septic shock diagnosis, treatment, and prognosis.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.14.296889v1?rss=1

Authors: Dogan, T., Atas, H., Joshi, V., Atakan, A., Rifaioglu, A. S., Nalbat, E., Nightingale, A., Saidi, R., Volynkin, V., Zellner, H., Cetin-Atalay, R., Martin, M., Atalay, V.

Abstract:

Systemic analysis of available large-scale biological and biomedical data is critical for developing novel and effective treatment approaches against both complex and infectious diseases. Owing to the fact that different sections of the biomedical data is produced by different organizations/institutions using various types of technologies, the data are scattered across individual computational resources, without any explicit relations/connections to each other, which greatly hinders the comprehensive multi-omics-based analysis of data. We aimed to address this issue by constructing a new biological and biomedical data resource, CROssBAR, a comprehensive system that integrates large-scale biomedical data from various resources and store them in a new NoSQL database, enrich these data with deep-learning-based prediction of relations between numerous biomedical entities, rigorously analyse the enriched data to obtain biologically meaningful modules and display them to users via easy-to-interpret, interactive and heterogenous knowledge graph (KG) representations within an open access, user-friendly and online web-service at https://crossbar.kansil.org. As a use-case study, we constructed CROssBAR COVID-19 KGs (available at: https://crossbar.kansil.org/covid_main.php) that incorporate relevant virus and host genes/proteins, interactions, pathways, phenotypes and other diseases, as well as known and completely new predicted drugs/compounds. Our COVID-19 graphs can be utilized for a systems-level evaluation of relevant virus-host protein interactions, mechanisms, phenotypic implications and potential interventions.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.15.298604v1?rss=1

Authors: Trinity, L., Lansing, L., Jabbari, H., Stege, U.

Abstract:

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the COVID-19 pandemic; a pandemic of a scale that has not been seen in the modern era. Despite over 29 million reported cases and over 900,000 deaths worldwide as of September 2020, herd immunity and widespread vaccination efforts by many experts are expected to be insufficient in addressing this crisis for the foreseeable future. Thus, there is an urgent need for treatments that can lessen the effects of SARS-CoV-2 in patients who become seriously affected. Many viruses including HIV, the common cold, SARS-CoV and SARS-CoV-2 use a unique mechanism known as -1 programmed ribosomal frameshifting (-1 PRF) to successfully replicate and infect cells in the human host. SARS-CoV (the coronavirus responsible for SARS) and SARS-CoV-2 possess a unique RNA structure, a three-stemmed pseudoknot, that stimulates -1 PRF. Recent experiments identified that small molecules can be introduced as antiviral agents to bind with the pseudoknot and disrupt its stimulation of -1 PRF. If successfully developed, small molecule therapy that targets -1 PRF in SARS-CoV-2 is an excellent strategy to improve patients' prognoses. Crucial to developing these successful therapies is modeling the structure of the SARS-CoV-2 -1 PRF pseudoknot. Following a structural alignment approach, we identify similarities in the -1 PRF pseudoknots of the novel coronavirus SARS-CoV-2, the original SARS-CoV, as well as a third coronavirus: MERS-CoV, the coronavirus responsible for Middle East Respiratory Syndrome (MERS). In addition, we provide a better understanding of the SARS-CoV-2 -1 PRF pseudoknot by comprehensively investigating the structural landscape using a hierarchical folding approach. Since understanding the impact of mutations is vital to long-term success of treatments that are based on predicted RNA functional structures, we provide insight on SARS-CoV-2 -1 PRF pseudoknot sequence mutations and their effect on the resulting structure and its function.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.13.295592v1?rss=1

Authors: Deng, F., Huang, J., Yuan, X., Cheng, C., Zhang, L.

Abstract:

Most of the biomedical datasets, including those of omics, population studies and surveys, are rectangular in shape and have few missing data. Recently, their sample sizes have grown significantly. Rigorous analyses on these large datasets demand considerably more efficient and more accurate algorithms. Machine learning (ML) algorithms have been used to classify outcomes in biomedical datasets, including random forests (RF), decision tree (DT), artificial neural networks (ANN) and support vector machine (SVM). However, their performance and efficiency in classifying multi-category outcomes in rectangular data are poorly understood. Therefore, we aimed to compare these metrics among the 4 ML algorithms. As an example, we created a large rectangular dataset using the female breast cancers in the Surveillance, Epidemiology, and End Results-18 (SEER-18) database which were diagnosed in 2004 and followed up until December 2016. The outcome was the 6-category cause of death, namely alive, non-breast cancer, breast cancer, cardiovascular disease, infection and other cause. We included 58 dichotomized features from ~53,000 patients. All analyses were performed using MatLab (version 2018a) and the 10-fold cross validation approach. The accuracy in classifying 6-category cause of death with DT, RF, ANN and SVM was 72.68%, 72.66%, 70.01% and 71.85%, respectively. Based on the information entropy and information gain of feature values, we optimized dimension reduction (i.e. reduce the number of features in models). We found 22 or more features were required to maintain the similar accuracy, while the running time decreased from 440s for 58 features to 90s for 22 features in RF, from 70s to 40s in ANN and from 440s to 80s in SVM. In summary, we here show that RF, DT, ANN and SVM had similar accuracy for classifying multi-category outcomes in this large rectangular dataset. Dimension reduction based on information gain will significantly increase efficiency while maintaining classification accuracy of the models.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.13.295535v1?rss=1

Authors: Song, Q., Su, J., Zhang, W.

Abstract:

Single-cell omics represent the fastest-growing genomics data type in the literature and the public genomics repositories. Leveraging the growing repository of labeled datasets and transferring labels from existing datasets to newly generated datasets will empower the exploration of the single-cell omics. The current label transfer methods have limited performance, largely due to the intrinsic heterogeneity and extrinsic differences between datasets. Here, we present a robust graph-based artificial intelligence model, single-cell Graph Convolutional Network (scGCN), to achieve effective knowledge transfer across disparate datasets. Benchmarked with other label transfer methods on totally 30 single cell omics datasets, scGCN has consistently demonstrated superior accuracy on leveraging cells from different tissues, platforms, and species, as well as cells profiled at different molecular layers. scGCN is implemented as an integrated workflow as a python software, which is available at https://github.com/QSong-github/scGCN.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.13.274779v1?rss=1

Authors: Blamey, B., Toor, S., Dahlö, M., Wieslander, H., Harrison, P. J., Sintorn, I.-M., Sabirsh, A., Wählby, C., Spjuth, O., Hellander, A.

Abstract:

This paper introduces the HASTE Toolkit, a cloud-native software toolkit capable of partitioning data streams in order to prioritize usage of limited resources. This in turn enables more efficient data-intensive experiments. We propose a model that introduces automated, autonomous decision making in data pipelines, such that a stream of data can be partitioned into a tiered or ordered data hierarchy. Importantly, the partitioning is online and based on data content rather than a priori metadata. At the core of the model are interestingness functions and policies. Interestingness functions assign a quantitative measure of interestingness to a single data object in the stream, an interestingness score. Based on this score, a policy guides decisions on how to prioritize computational resource usage for a given object. The HASTE Toolkit is a collection of tools to adapt data stream processing to this pipeline model. The result is smart data pipelines capable of effective or even optimal use of e.g. storage, compute and network bandwidth, to support experiments involving rapid processing of scientific data characterized by large individual data object sizes. We demonstrate the proposed model and our toolkit through two microscopy imaging case studies, each with their own interestingness functions, policies, and data hierarchies. The first deals with a high content screening experiment, where images are analyzed in an on-premise container cloud with the goal of prioritizing the images for storage and subsequent computation. The second considers edge processing of images for upload into the public cloud for a real-time control loop for a transmission electron microscope.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.13.295196v1?rss=1

Authors: Yan, Y., Huang, S.-Y.

Abstract:

Protein-protein interactions play a fundamental role in all cellular processes. Therefore, determining the structure of protein-protein complexes is crucial to understand their molecular mechanisms and develop drugs targeting the protein-protein interactions. Recently, deep learning has led to a breakthrough in intraprotein contact prediction, achieving an unusual high accuracy in recent CASP structure prediction challenges. However, due to the limited number of known homologous protein-protein interactions and the challenge to generate joint multiple sequence alignments (MSA) of two interacting proteins, the advances in inter-protein contact prediction remain limited. Here, we have proposed a deep learning model to predict inter-protein residue-residue contacts across homo-oligomeric protein interfaces, named as DeepHomo, by integrating evolutionary coupling, sequence conservation, distance map, docking pattern, and physic-chemical information of monomers. DeepHomo was extensively tested on both experimentally determined structures and realistic CASP-CAPRI targets. It was shown that DeepHomo achieved a high accuracy of >60% for the top predicted contact and outperformed state-of-the-art direct-coupling analysis (DCA) and machine learning (ML)-based approaches. Integrating predicted contacts into protein docking with blindly predicted monomer structures also significantly improved the docking accuracy. The present study demonstrated the success of DeepHomo in inter-protein contact prediction. It is anticipated that DeepHomo will have a far-reaching implication in the inter-protein contact and structure prediction for protein-protein interactions.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.13.295105v1?rss=1

Authors: Tarn, C., Zeng, W.-F., Fei, Z.-C., He, S.-M.

Abstract:

Spectrum prediction using deep learning has attracted a lot of attention in recent years, and has been used in the analysis of data-dependent and data-independent acquisition mass spectrometry. Although existing deep learning methods have greatly increased the prediction accuracy, there is still considerable space for improvement, which is presently limited by the difference of instrument types or settings. In this work, we use the few-shot learning method to fit the data online to make up for the shortcoming. We evaluate the method using 9 commonly used datasets, where the instruments include Velos, QE, Lumos, and ABSciex, with NCEs being differently set. Experimental results show that, on nearly all of the datasets, pDeep3 achieves significantly higher prediction accuracy, with extermly low costs. For example, on a trypsin dataset Pandey-Velos of Orbitrap Velos from Human Proteome Map, with only 100 randomly selected peptide-spectrum matches, it increases the accuracy from 49% to 85%, and only takes 7 seconds using CPU. Further, we also show that our design is sufficiently effective to fill the accuracy gap, and not demanding on data quality. Finally, using OpenSWATH and EncyclopeDIA, we present the potential advantages of pDeep3 for DIA data analysis. The source code of pDeep3 is available at http://pfind.ict.ac.cn/software/pDeep3.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.14.296921v1?rss=1

Authors: Olgun, G., Tastan, O.

Abstract:

The dysregulation of long non-coding RNAs' (lncRNAs) expressions has been implicated in cancer. Since most of the lncRNAs' are not functionally characterized well, investigating the set of perturbed lncRNAs are is challenging. Existing methods that inspect lncRNAs functionally rely on the co-expressed coding genes, which are far better characterized functionally. LncRNAs can be known to act as transcriptional regulators; they may activate or repress the neighborhood's coding genes on the genome. Based on this, in this work, we aim to analyze the deregulated lncRNAs in cancer by taking into account their ability to regulate nearby loci on the genome. We perform functional analysis on differentially expressed lncRNAs for 28 different cancers considering their adjacent coding genes. We identify that some deregulated lncRNAs are cancer-specific, but a substantial number of lncRNAs are shared across cancers. Next, we assess the similarities of the cancer types based on the functional enrichment of the deregulated lncRNA sets. We find some cancers are very similar in the functions and biological processes related to the deregulated lncRNAs. We observe that some of the cancers for which we find similarity can be linked through primary, metastatic site relations. We investigate the similarity of enriched functional terms for the deregulated lncRNAs and the mRNAs. We further assess the enriched functions' similarity to the functions and processes that the known cancer driver genes take place. We believe that our methodology help to understand the impact of the lncRNAs in cancer functionally.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.12.294322v1?rss=1

Authors: Joshi, A. A., Choi, S., Chong, M., Sonkar, G., Gonzalez-Martinez, J., Nair, D., Wisnowski, J., Haldar, J., Shattuck, D., Damasio, H., Leahy, R. M.

Abstract:

Brain atlases provide reference parcellations of the brain that are essential for population neuroimaging studies. We present a new high-resolution, single-subject atlas labeled into two parcellation schemes: 1) the anatomical BCI-DNI atlas, which was labeled based on known morphological and anatomical features; and 2) the hybrid USCBrain atlas, which used additional functional information to guide the sub-parcellation of the cerebral cortex. Particular attention was paid to the image acquisition, processing, and labeling methods to capture fine anatomical details, accommodating for the high-quality data common in recent imaging studies. A single-subject, high-resolution T1-weighted image was acquired and was then processed by an expert neuroanatomist using semi-automated methods in BrainSuite. The brain's features were meticulously extracted with manual corrections to bias-field and masking steps, thereby providing accurate tissue classification and anatomical surface modeling. Guided by sulcal and gyral landmarks, labeled anatomical regions were drawn manually on coronal single-slice images to generate the BCI-DNI atlas, which contains 66 cortical and 29 noncortical regions. The cortical regions were further sub-parcellated based on connectivity analysis of resting fMRI data from multiple subjects in the Human Connectome Project (HCP) database, which were coregistered to the single subject. The resulting USCBrain atlas contains a total of 130 cortical and 29 noncortical regions. In addition to the anatomical and functional parcellations, we also provide a delineation between sulcal valleys and gyral crowns, which offer an additional set of 26 sulcal subregions per hemisphere. The intended use of the USCBrain atlas is to label individual brains through structural coregistration. To assess utility, we computed the adjusted Rand indices between individual sub-parcellations obtained through structural-only coregistration to the USCBrain atlas and sub-parcellations obtained directly from each subject's resting fMRI data. The gyral sub-parcellations generated by atlas-based registration show variable but generally good overlap with the resting fMRI-based subdivisions. In addition to the crisp parcellations, a probabilistic map is included to provide users a quantitative measure of reliability for each gyral subdivision. Both atlases can be used with the BrainSuite, FreeSurfer, and FSL software packages.

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