PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.05.370312v1?rss=1

Authors: Ehrlich, R., Kamga, L., Gil, A., Luzuriaga, K., Selin, L., Ghersi, D.

Abstract:

Motivation: Computationally predicting the specificity of T cell receptors can be a powerful tool to shed light on the immune response against infectious diseases and cancers, autoimmunity, cancer immunotherapy, and immunopathology. With more T cell receptor sequence data becoming available, the need for bioinformatics approaches to tackle this problem is even more pressing. Here we present SwarmTCR, a method that uses labeled sequence data to predict the specificity of T cell receptors using a nearest-neighbor approach. SwarmTCR works by optimizing the weights of the individual CDR regions to maximize classification performance. Results: We compared the performance of SwarmTCR against a state-of-the-art method (TCRdist) and showed that SwarmTCR performed significantly better on epitopes EBV-BRLF1, NS4B with single cell data and epitopes EBV-BRLF1, IAV-M1 with bulk sequencing data (alpha and beta chains). In addition, we show that the weights returned by SwarmTCR are biologically interpretable. Availability: SwarmTCR is distributed freely under the terms of the GPL-3 license. The source code and all sequencing data are available at GitHub (https://github.com/thecodingdoc/SwarmTCR).

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.04.369033v1?rss=1

Authors: Heryanto, Y. D., Cheng, C.-Y., Uchida, Y., Mimura, K., Ishii, M., Yamada, R.

Abstract:

The cell movement and morphological change are two interrelated cellular processes. The integrated analysis is needed to explore the relationship between them. However, it has been challenging to investigate them as a whole. The cell trajectory can be described by its speed, curvature, and torsion. On the other hand, the 3-Dimensional (3D) cell shape can be studied by using a shape descriptor such as Spherical harmonic (SH) descriptor which is an extension of Fourier transform in 3D space. To integrate these shape-movement data, we propose a method to use a parallel-transport (PT) moving frame as the 3D-shape coordinate system. This moving frame is purely determined by the velocity vector. On this moving frame, the movement change will influence the coordinate system for shape analysis. By analyzing the change of the SH coefficients over time in the moving frame, we can observe the relationship between shape and movement. We illustrate the application of our approach using simulational and real datasets in this paper.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.05.368662v1?rss=1

Authors: Bassot, C., Govindarajan, S., Lamb, J., Elofsson, A.

Abstract:

Members of the CPA/AT transporter superfamily show significant structural variability. All previously known members consist of an inverted duplicated repeat unit that folds into two separate domains, the core and the scaffold domain. Crucial for its transporting function, the central helix in the core domain is a noncanonical transmembrane helix, which can either be in the form of a broken helix or a reentrant helix. Here, we expand the structural knowledge of the CPA/AT family by using contact-prediction-based protein modelling. We show that the N-terminal domains of the Pfam families; PSE (Cons_hypoth698 PF03601), Lysine exporter (PF03956) and LrgB (PF04172) families have a previously unseen reentrant-helix-reentrant fold. The close homology between PSE and the Sodium-citrate symporter (2HCT) suggests that the new fold originates from the truncation of an ancestral reentrant protein, caused by the loss of the C-terminal reentrant helix. To compensate for the lost reentrant helix one external loop moves into the membrane to form the second reentrant helix, highlighting the adaptability of the CPA/AT transporters. This study also demonstrates that the most recent deep-learning-based modelling methods have become a useful tool to gain biologically relevant structural, evolutionary and functional insights about protein families.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.04.367789v1?rss=1

Authors: Forni, G., Ruggeri, A. A., Piccinini, G., Luchetti, A.

Abstract:

Inferring the selective forces that different ortholog genes underwent across different lineages can make us understand the evolutionary processes which shaped their extant diversity. The more widespread metric to estimate coding sequences selection regimes across across their sites and species phylogeny is the ratio of nonsynonymous to synonymous substitutions (dN/dS, also known as {omega}). Nowadays, modern sequencing technologies and the large amount of already available sequence data allow the retrieval of thousands of genes orthology groups across large numbers of species. Nonetheless, the tools available to explore selection regimes are not designed to automatically process all orthogroups and practical usage is often restricted to those consisting of single-copy genes which are ubiquitous across the species considered (i.e. the subset of genes which is shared by all the species considered). This approach limits the scale of the analysis to a fraction of single-copy genes, which can be as lower as an order of magnitude in respect to non-ubiquitous ones (i.e. those which are not present across all the species considered). Here we present a workflow named BASE that - leveraging the CodeML framework - ease the inference and interpretation of selection regimes in the context of comparative genomics. Although a number of bioinformatics tools have already been developed to facilitate this kind of analyses, BASE is the first to be specifically designed to ease the integration of non-ubiquitous genes orthogroups. The workflow - along with all the relevant documentation - is available at github.com/for-giobbe/BASE.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.04.369108v1?rss=1

Authors: Chang, Y., Allen, C., Wan, C., Chung, D., Zhang, C., Li, Z., Ma, Q.

Abstract:

Single-cell RNA-Seq (scRNA-Seq) data is useful in discovering cell heterogeneity and signature genes in specific cell populations in cancer and other complex diseases. Specifically, the investigation of functional gene modules (FGM) can help to understand gene interactive networks and complex biological processes. QUBIC2 is recognized as one of the most efficient and effective tools for FGM identification from scRNA-Seq data. However, its limited availability to a C implementation restricted its application to only a few downstream analyses functionalities. We developed an R package named IRIS-FGM (Integrative scRNA-Seq Interpretation System for Functional Gene Module analysis) to support the investigation of FGMs and cell clustering using scRNA-Seq data. Empowered by QUBIC2, IRIS-FGM can effectively identify co-expressed and co-regulated FGMs, predict cell types/clusters, uncover differentially expressed genes, and perform functional enrichment analysis. It is noteworthy that IRIS-FGM can also takes Seurat objects as input, which facilitate easy integration with existing analysis pipeline.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.04.368225v1?rss=1

Authors: Boemo, M. A.

Abstract:

The detection of base analogues in Oxford Nanopore Technologies (ONT) sequencing reads has become a promising new method for the high-throughput measurement of DNA replication dynamics with single-molecule resolution. This paper introduces DNAscent v2, software that uses a residual neural network to achieve fast, accurate detection of the thymidine analogue BrdU with single-base resolution. DNAscent v2 comes equipped with an autoencoder that detects replication forks, origins, and termination sites in ONT sequencing reads from both synchronous and asynchronous cell populations, outcompeting previous versions and other tools across different experimental protocols. DNAscent v2 is open-source and available at https://github.com/MBoemo/DNAscent.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.02.365510v1?rss=1

Authors: Ren, Z., Gerlach, M., Shi, H., Misharin, A. V., Budinger, G. S., Amaral, L. A. N.

Abstract:

Single cell RNA sequencing (scRNA-seq), which promises to enable the quantitative study of biological processes at the single cell level, are now a routine part of experimental practice. One key computational challenge in analyzing scRNA-seq data is cell type annotation. A source of concern, however, is that the data analysis protocols for clustering cells suffer from low reproducibility and poorly-quantified accuracy. Here, we propose a new benchmark for determining clustering accuracy that uses a dataset where independent reference annotations are generated from surface protein measurements. We then systematically investigate the impact on labelling accuracy of different approaches to feature selection, of different clustering algorithms, and of different sets of parameter values. We demonstrate quantitatively the impact of feature selection and the poor performance of a widely used approach. Additionally, we show that an approach grounded on information theory can provide a generalizable, reliable, and accurate process for discarding uninformative features.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.02.365437v1?rss=1

Authors: Yu, F., Haynes, S. E., Nesvizhskii, A. I.

Abstract:

Missing values weaken the power of label-free quantitative proteomic experiments to uncover true quantitative differences between biological samples or experimental conditions. Match-between-runs (MBR) has become a common approach to mitigate the missing value problem, where peptides identified by tandem mass spectra in one run are transferred to another by inference based on m/z, charge state, retention time, and ion mobility when applicable. Though tolerances are used to ensure such transferred identifications are reasonably located and meet certain quality thresholds, little work has been done to evaluate the statistical confidence of MBR. Here, we present a mixture model-based approach to estimate the false discovery rate (FDR) of peptide and protein identification transfer, which we implement in the label-free quantification tool IonQuant. Using several benchmarking datasets generated on both Orbitrap and timsTOF mass spectrometers, we demonstrate that IonQuant with FDR-controlled MBR results in superior performance compared to MaxQuant. We further illustrate the need for FDR-controlled MBR in sparse datasets such as those from single-cell proteomics experiments.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.03.365585v1?rss=1

Authors: Gotti, C., Roux-Dalvai, F., Joly-Beauparlant, C., Leclercq, M., Mangnier, L., Droit, A.

Abstract:

In the past few years, LC-MS/MS in DIA mode has become a strategy of choice for deep coverage of complex proteomes and accurate quantification of low abundant species. However, there is still no consensus in the literature on the best acquisition parameters and processing tools to use. We present here the largest benchmark of DIA proteomic workflows on Orbitrap instruments ever published. Using a complex proteomic standard, we tested 36 workflows including 4 different acquisition schemes and 6 different software tools (DIA-NN, DIA-Umpire, OpenSWATH, Scaffold-DIA, Skyline and Spectronaut-Pulsar-X) with or without the use of a spectral library. By reporting the protein identifications, linearity, reproducibility, as well as sensitivity and specificity in 28 pairwise comparisons, we propose here a guide book for the users to choose the best DIA strategy for their data. Moreover, our data has been deposited on ProteomeXchange for testing or developing new DIA processing tools.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.03.365932v1?rss=1

Authors: Morton, J., Strauss, C., Blackwell, R., Berenberg, D., Gligorijevic, V., Bonneau, R.

Abstract:

Computing sequence similarity is a fundamental task in biology, with alignment forming the basis for the annotation of genes and genomes and providing the core data structures for evolutionary analysis. Standard approaches are a mainstay of modern molecular biology and rely on variations of edit distance to obtain explicit alignments between pairs of biological sequences. However, sequence alignment algorithms struggle with remote homology tasks and cannot identify similarities between many pairs of proteins with similar structures and likely homology. Recent work suggests that using machine learning language models can improve remote homology detection. To this end, we introduce DeepBLAST, that obtains explicit alignments from residue embeddings learned from a protein language model integrated into an end-to-end differentiable alignment framework. This approach can be accelerated on the GPU architectures and outperforms conventional sequence alignment techniques in terms of both speed and accuracy when identifying structurally similar proteins.

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