PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.28.317719v1?rss=1

Authors: Joshy, N., Yun, K.

Abstract:

Motivation: Single-cell RNA sequencing (scRNA-seq) is a recent technology that has provided many valuable biological insights. Notable uses include identifying novel cell-types, measuring the cellular response to treatment, and tracking trajectories of distinct cell lineages in time. The raw data generated in this process typically amounts to hundreds of millions of sequencing reads and requires substantial computational infrastructure for downstream analysis, a major hurdle for a biological research lab. Fortunately, the preprocessing step that converts this huge sequence data into manageable cell-specific expression profiles is standardized and can be performed in the cloud. We demonstrate how a cloud-based computational framework can be used to transform the raw data into biologically interpretable cell-type-specific information, using either 3 or 5 transcriptome libraries from 10x Genomics. The processed data which is an order of magnitude smaller in size can be easily downloaded to a laptop for customized analysis to gain deeper biological insights. Results: We produced an automated and easily extensible pipeline in the cloud for the analysis of single-cell RNA-seq data which provides a convenient method to handle post-processing of scRNA sequencing using next generation sequencing platforms. The basic step provides the transformation of the scRNA-seq data to cell-type-specific expression profiles and computes the quality control metrics for the dataset. The extensibility of the platform is demonstrated by adding a doublet-removal algorithm and recomputing the clustering of the cells. Any additional computational steps that take a cell-type expression counts matrix as input can be easily added to this framework with minimal effort. Availability: The framework and its documentation for installation is available at the Github repository http://github.com/nj3252/CB-Source/

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.28.282699v1?rss=1

Authors: Langenstein, M., Hermjakob, H., Bernal Llinares, M.

Abstract:

Motivation: Curation is essential for any data platform to maintain the quality of the data it provides. Existing databases, which require maintenance, and the amount of newly published information that needs to be surveyed, are growing rapidly. More efficient curation is often vital to keep up with this growth, requiring modern curation tools. However, curation interfaces are often complex and difficult to further develop. Furthermore, opportunities for experimentation with curation workflows may be lost due to a lack of development resources, or a reluctance to change sensitive production systems. Results: We propose a decoupled, modular and scriptable architecture to build curation tools on top of existing platforms. Instead of modifying the existing infrastructure, our architecture treats the existing platform as a black box and relies only on its public APIs and web application. As a decoupled program, the tool's architecture gives more freedom to developers and curators. This added flexibility allows for quickly prototyping new curation workflows as well as adding all kinds of analysis around the data platform. The tool can also streamline and enhance the curator's interaction with the web interface of the platform. We have implemented this design in cmd-iaso, a command-line curation tool for the identifiers.org registry.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.28.316372v1?rss=1

Authors: Lavrichenko, K., Helgeland, O., Njolstad, P. R., Jonassen, I., Johansson, S.

Abstract:

Motivation: Single nucleotide polymorphism (SNP) genotyping arrays remain an attractive platform for assaying copy number variants (CNVs) in large population-wide cohorts. However current tools for calling CNVs are still prone to extensive false positive calls when applied to biobank scale arrays. Moreover, there is a lack of methods exploiting cohorts with trios available (e.g. nuclear family) to assist in quality control and downstream analyses following the calling. Results: We developed SeeCiTe (Seeing Cnvs in Trios), a novel CNV quality control tool that post-processes output from current CNV calling tools exploiting child-parent trio data to classify calls in quality categories and provide a set of visualizations for each putative CNV call in the offspring. We apply it to the Norwegian Mother, Father, and Child Cohort Study (MoBa) and show that SeeCiTe improves the specificity and sensitivity compared to the common empiric filtering strategies. To our knowledge it is the first tool that utilizes probe-level CNV data in trios to systematically highlight potential artefacts and visualize signal intensities in a streamlined fashion suitable for biobank scale studies. Availability and Implementation: The software is implemented in R with the source code freely available at https://github.com/aksenia/SeeCiTe.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.27.316067v1?rss=1

Authors: Xin, Z., Cai, Y., Dang, L. T., Burke, H. M. S., Revote, J., Nim, H. T., Li, Y.-F., Ramialison, M.

Abstract:

MonaGO is a novel web-based visualisation system that provides an intuitive, interactive and responsive interface for performing gene ontology (GO) enrichment analysis and visualising the results. MonaGO combines dynamic clustering and interactive visualisation as well as customisation options to assist biologists in obtaining meaningful representation of overrepresented GO terms, producing simplified outputs in an unbiased manner. MonaGO supports gene lists as well as GO terms as inputs. Visualisation results can be exported as high-resolution images or restored in new sessions, allowing reproducibility of the analysis. An extensive comparison between MonaGO and 11 state-of-the-art GO enrichment visualisation tools based on 9 features revealed that MonaGO is the only platform that simultaneously allows interactive visualisation within one single output page, directly accessible through a web browser with customisable display options. In summary, MonaGO will facilitate the interpretation of GO analysis and will assist the biologists into the representation of the results.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.28.317461v1?rss=1

Authors: Aghasafari, P., Yang, P.-C., Kernik, D. C., Sakamoto, K., Kanda, Y., Kurokawa, J., Vorobyov, I., Clancy, C. E.

Abstract:

Exciting developments in both in vitro and in silico technologies have led to new ways to identify patient specific cardiac mechanisms. The development of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) has been a critical in vitro advance in the study of patient-specific physiology, pathophysiology and response to drugs. However, the iPSC-CM methodology is limited by the low throughput and high variability of resulting electrophysiological measurements. Moreover, the iPSC-CMs generate immature action potentials, and it is not clear if observations in the iPSC-CM model system can be confidently interpreted to reflect impact in human adults. There has been no demonstrated method to allow reliable translation of results from the iPSC-CM to a mature adult cardiac response. Here, we demonstrate a new computational approach intended to address the current shortcomings of the iPSC-CM platform by developing and deploying a multitask network that was trained and tested using simulated data and then applied to experimental data. We showed that a deep learning network can be applied to classify cells into the drugged and drug free categories and can be used to predict the impact of electrophysiological perturbation across the continuum of aging from the immature iPSC-CM action potential to the adult ventricular myocyte action potential. We validated the output of the model with experimental data. The method can be applied broadly across a spectrum of aging, but also to translate data between species.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.28.310482v1?rss=1

Authors: Broll, S., Urbanek, J., Buchanan, D., Chun, E., Muschelli, J., Punjabi, N., Gaynanova, I.

Abstract:

Continuous Glucose Monitoring (CGM) data play an increasing role in clinical practice as they provide detailed quantification of blood glucose levels during the entire 24-hour period. The R package iglu implements a wide range of CGM-derived metrics for measuring glucose control and glucose variability. The package also allows to visualize CGM data using time-series and lasagna plots. A distinct advantage of iglu is that it comes with a point-and-click graphical user interface (GUI), which makes the package widely accessible to users regardless of their programming experience. Thus, open-source and easy to use iglu package will help advance CGM research and CGM data analyses. R package iglu is publicly available on CRAN and at https://github.com/irinagain/iglu.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.25.311142v1?rss=1

Authors: Agastheeswaramoorthy, K., Sevilimedu, A.

Abstract:

Drug repositioning is emerging as an increasingly relevant option for rare disease therapy and management. Various methods for identifying suitable drug candidates have been tried and range from clinical symptomatic repurposing to data driven strategies which are based on the disease-specific gene or protein expression, modification, signalling and physiological perturbation profiles. The use of Artificial Intelligence (AI) and machine learning algorithms (ML) allows one to combine diverse data sets, and extract disease-specific data profiles which may not be intuitive or apparent from a subset of data. In this case study with Fragile X syndrome and autism, we have used multiple computational methodologies to extract profiles, which are then combined to arrive at a comprehensive signature (disease DEG). This DEG was then used to interrogate the large collection of drug-induced perturbation profiles present in public databases, to find appropriate small molecules to reverse or mimic the disease-profiles. We have labelled this pipeline Drug Repurposing using AI/ML tools - for Rare Diseases (DREAM-RD). We have shortlisted over 100 FDA approved drugs using the aforementioned pipeline, which may potentially be useful to ameliorate autistic phenotypes associated with FXS.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.25.313668v1?rss=1

Authors: Ahmed, S., Muhammod, R., Adilina, S., Khan, Z. H., Shatabda, S., Dehzangi, A.

Abstract:

Although advancing the therapeutic alternatives for treating deadly cancers has gained much attention globally, still the primary methods such as chemotherapy have significant downsides and low specificity. Most recently, Anticancer peptides (ACPs) have emerged as a potential alternative to therapeutic alternatives with much fewer negative side-effects. However, the identification of ACPs through wet-lab experiments is expensive and time-consuming. Hence, computational methods have emerged as viable alternatives. During the past few years, several computational ACP identification techniques using hand-engineered features have been proposed to solve this problem. In this study, we propose a new multi headed deep convolutional neural network model called ACP-MHCNN, for extracting and combining discriminative features from different information sources in an interactive way. Our model extracts sequence, physicochemical, and evolutionary based features for ACP identification through simultaneous interaction with different numerical peptide representations while restraining parameter overhead. It is evident through rigorous experiments using cross-validation and independent-dataset that ACP-MHCNN outperforms other models for anticancer peptide identification by a substantial margin. ACP-MHCNN outperforms state-of-the-art model by 6.3%, 8.6%, 3.7%, 4.0%, and 0.20 in terms of accuracy, sensitivity, specificity, precision, and MCC respectively. ACP-MHCNN and its relevant codes and datasets are publicly available at: https://github.com/mrzResearchArena/Anticancer-Peptides-CNN.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.25.313635v1?rss=1

Authors: Wani, A. H., Aiello, A. E., Kim, G. S., Xue, F., Martin, C. L., Ratanatharathorn, A., Qu, A., Koenen, K., Galea, S., Wildman, D. E., Uddin, M.

Abstract:

Background: A range of factors have been identified that contribute to greater incidence, severity, and prolonged course of post-traumatic stress disorder (PTSD), including: comorbid and/or prior psychopathology; social adversity such as low socioeconomic position, perceived discrimination, and isolation; and biological factors such as genomic variation at glucocorticoid receptor regulatory network (GRRN) genes. This complex etiology and clinical course make identification of people at higher risk of PTSD challenging. Here we leverage machine learning (ML) approaches to identify a core set of factors that may together predispose persons to PTSD. Methods: We used multiple ML approaches to assess the relationship among DNA methylation (DNAm) at GRRN genes, prior psychopathology, social adversity, and prospective risk for PTS severity (PTSS). Results: ML models predicted prospective risk of PTSS with high accuracy. The Gradient Boost approach was the top-performing model with mean absolute error of 0.135, mean square error of 0.047, root mean square error of 0.217, and R2 of 95.29%. Prior PTSS ranked highest in predicting the prospective risk of PTSS, accounting for >88% of the prediction. The top ranked GRRN CpG site was cg05616442, in AKT1, and the top ranked social adversity feature was loneliness. Conclusion: Multiple factors including prior PTSS, social adversity, and DNAm play a role in predicting prospective risk of PTSS. ML models identified factors accounting for increased PTSS risk with high accuracy, which may help to target risk factors that reduce the likelihood or course of PTSD, potentially pointing to approaches that can lead to early intervention.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.27.315937v1?rss=1

Authors: Cao, Y., Shen, Y.

Abstract:

Motivation: Facing the increasing gap between high-throughput sequence data and limited functional insights, computational protein function annotation provides a high-throughput alternative to experimental approaches. However, current methods can have limited applicability while relying on data besides sequences, or lack generalizability to novel sequences, species and functions. Results: To overcome aforementioned barriers in applicability and generalizability, we propose a novel deep learning model, named Transformer-based protein function Annotation through joint sequence--Label Embedding (TALE). For generalizbility to novel sequences we use self attention-based transformers to capture global patterns in sequences. For generalizability to unseen or rarely seen functions, we also embed protein function labels (hierarchical GO terms on directed graphs) together with inputs/features (sequences) in a joint latent space. Combining TALE and a sequence similarity-based method, TALE+ outperformed competing methods when only sequence input is available. It even outperformed a state-of-the-art method using network information besides sequence, in two of the three gene ontologies. Furthermore, TALE and TALE+ showed superior generalizability to proteins of low homology and never/rarely annotated novel species or functions compared to training data, revealing deep insights into the protein sequence-function relationship. Ablation studies elucidated contributions of algorithmic components toward the accuracy and the generalizability.

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