PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.11.245852v1?rss=1

Authors: Necci, M., Piovesan, D., CAID Predictors,, DisProt Curators,, Tosatto, S. C. E.

Abstract:

Intrinsically disordered proteins defying the traditional protein structure-function paradigm represent a challenge to study experimentally. As a large part of our knowledge rests on computational predictions, it is crucial for their accuracy to be high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in predicting intrinsically disordered regions in proteins and the subset of disordered residues involved in binding other molecules. A total of 43 methods, 32 for disorder and 11 for binding regions, were evaluated on a dataset of 646 proteins. The best methods use modern machine learning techniques and significantly outperform widely used first-generation methods across different types of targets. Disordered binding regions remain hard to predict correctly, which offers significant potential for improvement. Intriguingly, some of the top performing methods are also among the fastest.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.11.246892v1?rss=1

Authors: Almodaresi, F., Zakeri, M., Patro, R.

Abstract:

Motivation: Sequence alignment is one of the first steps in many modern genomic analyses, such as variant detection, transcript abundance estimation and metagenomic profiling. Unfortunately, it is often a computationally expensive procedure. As the quantity of data and wealth of different assays and applications continue to grow, the need for accurate and fast alignment tools persists. Results: In this paper, we introduce PuffAligner, a fast, accurate and versatile aligner built on top of the Pufferfish index. PuffAligner is able to produce highly-sensitive alignments, similar to those of Bowtie2, but much more quickly. While exhibiting similar speed to the ultrafast STAR aligner, PuffAligner requires considerably less memory to construct its index and align reads. PuffAligner strikes a desirable balance with respect to the time, space, and accuracy tradeoffs made by different alignment tools, and provides a promising foundation on which to test new alignment ideas over large collections of sequences. Availability: PuffAligner is a free and open-source software. It is implemented in C++14 and can be obtained from https://github.com/COMBINE-lab/pufferfish/tree/cigar-strings

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.11.247072v1?rss=1

Authors: Hie, B., Bryson, B. D., Berger, B.

Abstract:

Machine learning that generates biological hypotheses has transformative potential, but most learning algorithms are susceptible to pathological failure when exploring regimes beyond the training data distribution. A solution is to quantify prediction uncertainty so that algorithms can gracefully handle novel phenomena that confound standard methods. Here, we demonstrate the broad utility of robust uncertainty prediction in biological discovery. By leveraging Gaussian process-based uncertainty prediction on modern pretrained features, we train a model on just 72 compounds to make predictions over a 10,833-compound library, identifying and experimentally validating compounds with nanomolar affinity for diverse kinases and whole-cell growth inhibition of Mycobacterium tuberculosis. We show how uncertainty facilitates a tight iterative loop between computation and experimentation, improves the generative design of novel biochemical structures, and generalizes across disparate biological domains. More broadly, our work demonstrates that uncertainty should play a key role in the increasing adoption of machine learning algorithms into the experimental lifecycle.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.12.241976v1?rss=1

Authors: Fadl, H. A. O., Abdelmoneim, A. H., Elbager, S. G.

Abstract:

ABSTRACT Background: CLL: Chronic lymphocytic leukemia is a chronic type of haematological malignancies that evoked from lymph proliferative origin of bone marrow and secondary lymphoid tissue, resultant in proliferation and progressive accumulation of distinct monoclonal CD5 /CD19 /CD23 B lymphocytes in the bone marrow, peripheral blood, and lymphatic organs. CD38 is a multifunctional ecto-enzyme, known to be a direct contributor in pathogenesis of CLL by poorly understood mechanism. Even though , it highly expressed in CLL. At specific position of CD38 gene sequence, substitution of single nucleotide may result in change in amino acid that ends by consequent alteration of protein structure. Aim: To study CD38 polymorphism and to predict its effect on structure and subsequently function of CD38 molecule. Methodology and Result: The bioinformatic analysis of CD38 gene had been carried out by using several soft wares. Functional analysis by SIFT,Polyphen2, and PROVEAN reveled 12 deleterious SNPs. These SNPs were further analyzed by SNAP2, SNP@GO. PMut, STRING and other soft wars. Furthermore, Stability analysis was done using I-Mutant and MUpro software where seven SNPs were found to decrease the stability of the protein by I-Mutant ,while two SNPs increase it. At the same time, eight SNPs were found to decrease the stability by Mupro software while only one SNP is predicted to increase it. Finally, Physiochemical analysis was done using Project Hope. Conclusion: In summary, CD38 genotype seems to have twelve SNP that possibly will result in deleterious effect on Protein Structure. This genetic variation eventually will lead to alteration in potential molecule functions .Which effect the progression of CLL By the end. Keywords: B-Chronic lymphocytic leukemia, missense single nucleotide polymorphism, and CD38 gene.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.11.245712v1?rss=1

Authors: Meyer, F., Lesker, T. R., Koslicki, D., Fritz, A., Gurevich, A., Darling, A. E., Sczyrba, A., Bremges, A., McHardy, A. C.

Abstract:

Computational methods are key in microbiome research, and obtaining a quantitative and unbiased performance estimate is important for method developers and applied researchers. For meaningful comparisons between methods, to identify best practices, common use cases, and to reduce overhead in benchmarking, it is necessary to have standardized data sets, procedures, and metrics for evaluation. In this tutorial, we describe emerging standards in computational metaomics benchmarking derived and agreed upon by a larger community of researchers. Specifically, we outline recent efforts by the Critical Assessment of Metagenome Interpretation (CAMI) initiative, which supplies method developers and applied researchers with exhaustive quantitative data about software performance in realistic scenarios and organizes community-driven benchmarking challenges. We explain the most relevant evaluation metrics to assess metagenome assembly, binning, and profiling results, and provide step-by-step instructions on how to generate them. The instructions use simulated mouse gut metagenome data released in preparation for the second round of CAMI challenges and showcase the use of a repository of tool results for CAMI data sets. This tutorial will serve as a reference to the community and facilitate informative and reproducible benchmarking in microbiome research.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.11.247437v1?rss=1

Authors: Chen, C., Shi, H., Han, Y., Jiang, Z., Cui, X., Yu, B.

Abstract:

Research, analysis, and prediction of drug-target interactions (DTIs) play an important role in understanding drug mechanisms, drug repositioning and design. Machine learning (ML)-based methods for DTIs prediction can mitigate the shortcomings of time-consuming and labor-intensive experimental approaches, providing new ideas and insights for drug design. We propose a novel pipeline for predicting drug-target interactions, called DNN-DTIs. First, the target information is characterized by pseudo-amino acid composition, pseudo position-specific scoring matrix, conjoint triad, composition, transition and distribution, Moreau-Broto autocorrelation, and structure feature. Then, the drug compounds are encoded using substructure fingerprint. Next, we utilize XGBoost to determine nonredundant and important feature subset, then the optimized and balanced sample vectors could be obtained through SMOTE. Finally, a DTIs predictor, DNN-DTIs, is developed based on deep neural network (DNN) via layer-by-layer learning. Experimental results indicate that DNN-DTIs achieves outstanding performance than other predictors with the ACC values of 98.78%, 98.60%, 97.98%, 98.24% and 98.00% on Enzyme, Ion Channels (IC), GPCR, Nuclear Receptors (NR) and Kuang's dataset. Therefore, DNN-DTIs's accurate prediction performance on Network1 and Network2 make it logical choice for contributing to the study of DTIs, especially, the drug repositioning and new usage of old drugs.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.12.243121v1?rss=1

Authors: Sharma, R., Pandey, N., Mongia, A., Mishra, S., Majumdar, A., KUMAR, V.

Abstract:

The advent of single-cell open-chromatin profiling technology has facilitated the analysis of heterogeneity of activity of regulatory regions at single-cell resolution. However, stochasticity and availability of low amount of relevant DNA cause high drop-out rate and noise in single-cell open-chromatin profiles. We introduce here a robust method called as Forest of Imputation Trees (FITs) to recover original signals from highly sparse and noisy single-cell open-chromatin profiles. FITs makes a forest of imputation trees to avoid bias during the restoration of read-count matrices. It resolves the challenging issue of recovering open chromatin signals without blurring out information at genomic sites with cell-type-specific activity. FITs is generalized for wider applicability, especially for highly sparse read-count matrices. The superiority of FITs in recovering signals of minority cells also makes it highly useful for single-cell open-chromatin profile from in vivo samples.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.11.245969v1?rss=1

Authors: Szikriszt, B., Poti, A., Nemeth, E., Kanu, N., Swanton, C., Szuts, D.

Abstract:

Platinum-based drugs are a mainstay of cancer chemotherapy. However, their mutagenic effect can increase tumour heterogeneity, contribute to the evolution of treatment resistance, and also induce secondary malignancies. We coupled whole genome sequencing with phenotypic investigations on two cell line models to compare the magnitude and understand the mechanism of mutagenicity of cisplatin, carboplatin and oxaliplatin. Cisplatin induced significantly more base substitution mutations than carboplatin or oxaliplatin when used at equitoxic concentrations on human TK6 or chicken DT40 cells, and also induced the highest number of short insertions and deletions. Assessment through histone H2AX phosphorylation and single cell agarose gel electrophoresis suggested that cisplatin caused more DNA damage than carboplatin or oxaliplatin. The analysis of base substitution spectra revealed that all three tested platinum drugs elicit both a direct mutagenic effect at purine dinucleotides, and an indirect effect of accelerating endogenous mutagenic processes. Whereas the direct mutagenic effect correlated with the level of DNA damage caused, the indirect mutagenic effects were equal. The different mutagenicity and DNA damaging effect of equitoxic platinum drug treatments suggests that DNA damage independent mechanisms significantly contribute to their cytotoxicity. Thus, the comparatively high mutagenicity of cisplatin should be taken into account in the design of chemotherapeutic regimens.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.11.245621v1?rss=1

Authors: Vantini, M., Mannerström, H., Rautio, S., Ahlfors, H., Stockinger, B., Lähdesmäki, H.

Abstract:

We propose PairGP, a non-stationary Gaussian process method to compare gene expression time-series across several conditions that can account for paired longitudinal study designs and can identify groups of conditions that have different gene expression dynamics. We demonstrate the method on both simulated data and previously unpublished RNA-seq time-series with five conditions. The results show the advantage of modeling the pairing effect to better identify groups of conditions with different dynamics. The implementations is available at https://github.com/michelevantini/PairGP

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.11.245795v1?rss=1

Authors: Maedler, S. C., Julien-Laferriere, A., Wyss, L., Phan, M., Kang, A. S. W., Ulrich, E., Schmucki, R., Zhang, J. D., Ebeling, M., Badi, L., Kam-Thong, T., Schwalie, P. C., Hatje, K.

Abstract:

Single-cell RNA sequencing (scRNA-seq) revolutionised our understanding of disease biology and presented the promise of transforming translational research. We developed Besca, a toolkit that streamlines scRNA-seq analyses according to current best practices. A standard workflow covers quality control, filtering, and clustering. Two complementary Besca modules, utilizing hierarchical cell signatures or supervised machine learning, automate cell annotation and provide harmonised nomenclatures across studies. Subsequently, Besca enables estimation of cell type proportions in bulk transcriptomics studies. Using multiple heterogeneous scRNA-seq datasets we show how Besca aids acceleration, interoperability, reusability, and interpretability of scRNA-seq data analysis, crucial aspects in translational research and beyond.

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