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1. Unification of miRNA and isomiR research: the mirGFF3 format and the mirtop API

   https://doi.org/10.1093/bioinformatics/btz675  

   Desvignes, Thomas ; Loher, Phillipe ; Eilbeck, Karen ; Ma, Jeffery ; Urgese, Gianvito ; Fromm, Bastian ; Sydes, Jason ; Aparicio-Puerta, Ernesto ; Barrera, Victor ; Espín, Roderic ; et al ( August 2019 , Bioinformatics)

   MicroRNAs (miRNAs) are small RNA molecules (∼22 nucleotide long) involved in post-transcriptional gene regulation. Advances in high-throughput sequencing technologies led to the discovery of isomiRs, which are miRNA sequence variants. While many miRNA-seq analysis tools exist, the diversity of output formats hinders accurate comparisons between tools and precludes data sharing and the development of common downstream analysis methods.

   To overcome this situation, we present here a community-based project, miRNA Transcriptomic Open Project (miRTOP) working towards the optimization of miRNA analyses. The aim of miRTOP is to promote the development of downstream isomiR analysis tools that are compatible with existing detection and quantification tools. Based on the existing GFF3 format, we first created a new standard format, mirGFF3, for the output of miRNA/isomiR detection and quantification results from small RNA-seq data. Additionally, we developed a command line Python tool, mirtop, to create and manage the mirGFF3 format. Currently, mirtop can convert into mirGFF3 the outputs of commonly used pipelines, such as seqbuster, isomiR-SEA, sRNAbench, Prost! as well as BAM files. Some tools have also incorporated the mirGFF3 format directly into their code, such as, miRge2.0, IsoMIRmap and OptimiR. Its open architecture enables any tool or pipeline to output or convert results into mirGFF3. Collectively, this isomiR categorization system, along with the accompanying mirGFF3 and mirtop API, provide a comprehensive solution for the standardization of miRNA and isomiR annotation, enabling data sharing, reporting, comparative analyses and benchmarking, while promoting the development of common miRNA methods focusing on downstream steps of miRNA detection, annotation and quantification.

   https://github.com/miRTop/mirGFF3/ and https://github.com/miRTop/mirtop.

   desvignes@uoneuro.uoregon.edu or lpantano@iscb.org

   Supplementary data are available at Bioinformatics online.

    

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2. Neurotransmission and neuromodulation systems in the learning and memory network of Octopus vulgaris

   https://doi.org/10.1002/jmor.21459  

   Stern-Mentch, N. ; Winter, G. ; Belenky, M. ; Moroz, L.L. ; Hochner, B. ( October 2021 , bioRxiv)

   The vertical lobe (VL) in the octopus brain plays an essential role in its sophisticated learning and memory. Early anatomical studies suggested that the VL is organized in a “fan-out fan-in” connectivity matrix comprising only three morphologically identified neuron types; input axons from the superior frontal lobe (SFL) innervating en passant millions of small amacrine interneurons (AMs) which converge sharply onto large VL output neurons (LNs). Recent physiological studies confirmed the feedforward excitatory connectivity: a glutamatergic synapse at the first SFL-to-AM synaptic layer and a cholinergic AM-to-LNs synapse. SFL-to-AMs synapses show a robust hippocampal-like activity-dependent long-term potentiation (LTP) of transmitter release. 5-HT, octopamine, dopamine, and nitric oxide modulate short- and long-term VL synaptic plasticity. Here we present a comprehensive histolabeling study to better characterize the neural elements in the VL. We generally confirmed glutamatergic SFLs and cholinergic AMs. Intense labeling for NOS activity in the AMs neurites fitted with the NO-dependent presynaptic LTP mechanism at the SFL-to-AM synapse. New discoveries here reveal more heterogeneity of the VL neurons than previously thought. GABAergic AMs suggest a subpopulation of inhibitory interneurons in the first input layer. Clear GABA labeling in the cell bodies of LNs supported an inhibitory VL output yet the LNs co-expressed FMRFamide-like neuropeptides suggesting an additional neuromodulatory role of the VL output. Furthermore, a group of LNs was glutamatergic. A new cluster of cells organized in a “deep nucleus” showed rich catecholaminergic labeling and may play a role in intrinsic neuromodulation. In situ hybridization and immunolabeling allowed characterization and localization of a rich array of neuropeptides and neuromodulators, likely involved in reward/punishment signals. This analysis of the fast transmission system, together with the newly found cellular elements helps integrate behavioral, physiological, pharmacological, and connectome findings into a more comprehensive understanding of an efficient learning and memory network. 

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3. Deep learning-based subdivision approach for large scale macromolecules structure recovery from electron cryo tomograms

   https://doi.org/10.1093/bioinformatics/btx230  

   Xu, Min ; Chai, Xiaoqi ; Muthakana, Hariank ; Liang, Xiaodan ; Yang, Ge ; Zeev-Ben-Mordehai, Tzviya ; Xing, Eric P. ( July 2017 , Bioinformatics)

   Cellular Electron CryoTomography (CECT) enables 3D visualization of cellular organization at near-native state and in sub-molecular resolution, making it a powerful tool for analyzing structures of macromolecular complexes and their spatial organizations inside single cells. However, high degree of structural complexity together with practical imaging limitations makes the systematic de novo discovery of structures within cells challenging. It would likely require averaging and classifying millions of subtomograms potentially containing hundreds of highly heterogeneous structural classes. Although it is no longer difficult to acquire CECT data containing such amount of subtomograms due to advances in data acquisition automation, existing computational approaches have very limited scalability or discrimination ability, making them incapable of processing such amount of data.

   To complement existing approaches, in this article we propose a new approach for subdividing subtomograms into smaller but relatively homogeneous subsets. The structures in these subsets can then be separately recovered using existing computation intensive methods. Our approach is based on supervised structural feature extraction using deep learning, in combination with unsupervised clustering and reference-free classification. Our experiments show that, compared with existing unsupervised rotation invariant feature and pose-normalization based approaches, our new approach achieves significant improvements in both discrimination ability and scalability. More importantly, our new approach is able to discover new structural classes and recover structures that do not exist in training data.

   Source code freely available at http://www.cs.cmu.edu/∼mxu1/software.

   Supplementary data are available at Bioinformatics online.

    

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4. DEIsoM: a hierarchical Bayesian model for identifying differentially expressed isoforms using biological replicates

   https://doi.org/10.1093/bioinformatics/btx357  

   Peng, Hao ; Yang, Yifan ; Zhe, Shandian ; Wang, Jian ; Gribskov, Michael ; Qi, Yuan ; Valencia, ed., Alfonso ( June 2017 , Bioinformatics)

   High-throughput mRNA sequencing (RNA-Seq) is a powerful tool for quantifying gene expression. Identification of transcript isoforms that are differentially expressed in different conditions, such as in patients and healthy subjects, can provide insights into the molecular basis of diseases. Current transcript quantification approaches, however, do not take advantage of the shared information in the biological replicates, potentially decreasing sensitivity and accuracy.

   We present a novel hierarchical Bayesian model called Differentially Expressed Isoform detection from Multiple biological replicates (DEIsoM) for identifying differentially expressed (DE) isoforms from multiple biological replicates representing two conditions, e.g. multiple samples from healthy and diseased subjects. DEIsoM first estimates isoform expression within each condition by (1) capturing common patterns from sample replicates while allowing individual differences, and (2) modeling the uncertainty introduced by ambiguous read mapping in each replicate. Specifically, we introduce a Dirichlet prior distribution to capture the common expression pattern of replicates from the same condition, and treat the isoform expression of individual replicates as samples from this distribution. Ambiguous read mapping is modeled as a multinomial distribution, and ambiguous reads are assigned to the most probable isoform in each replicate. Additionally, DEIsoM couples an efficient variational inference and a post-analysis method to improve the accuracy and speed of identification of DE isoforms over alternative methods. Application of DEIsoM to an hepatocellular carcinoma (HCC) dataset identifies biologically relevant DE isoforms. The relevance of these genes/isoforms to HCC are supported by principal component analysis (PCA), read coverage visualization, and the biological literature.

   The software is available at https://github.com/hao-peng/DEIsoM

   Supplementary data are available at Bioinformatics online.

    

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5. 3DClusterViSu: 3D clustering analysis of super-resolution microscopy data by 3D Voronoi tessellations

   https://doi.org/10.1093/bioinformatics/bty200  

   Andronov, Leonid ; Michalon, Jonathan ; Ouararhni, Khalid ; Orlov, Igor ; Hamiche, Ali ; Vonesch, Jean-Luc ; Klaholz, Bruno P. ; Murphy, ed., Robert ( April 2018 , Bioinformatics)

   Single-molecule localization microscopy (SMLM) can play an important role in integrated structural biology approaches to identify, localize and determine the 3D structure of cellular structures. While many tools exist for the 3D analysis and visualization of crystal or cryo-EM structures little exists for 3D SMLM data, which can provide unique insights but are particularly challenging to analyze in three dimensions especially in a dense cellular context.

   We developed 3DClusterViSu, a method based on 3D Voronoi tessellations that allows local density estimation, segmentation and quantification of 3D SMLM data and visualization of protein clusters within a 3D tool. We show its robust performance on microtubules and histone proteins H2B and CENP-A with distinct spatial distributions. 3DClusterViSu will favor multi-scale and multi-resolution synergies to allow integrating molecular and cellular levels in the analysis of macromolecular complexes.

   3DClusterViSu is available under http://cbi-dev.igbmc.fr/cbi/voronoi3D.

   Supplementary figures are available at Bioinformatics online.

    

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