scholarly journals Spacemake: processing and analysis of large-scale spatial transcriptomics data

2021 ◽  
Author(s):  
Tamas Ryszard Sztanka-Toth ◽  
Marvin Jens ◽  
Nikos Karaiskos ◽  
Nikolaus Rajewsky
Keyword(s):  
Large Scale ◽  
Modular Design ◽  
State Of The Art ◽  
Sequencing Data ◽  
Unified Framework ◽  
Tissue Sections ◽  
Long Reads ◽  
Rna Biology ◽  
Transcriptomics Data ◽  
Downstream Analysis

Spatial sequencing methods increasingly gain popularity within RNA biology studies. State-of-the-art techniques can read mRNA expression levels from tissue sections and at the same time register information about the original locations of the molecules in the tissue. The resulting datasets are processed and analyzed by accompanying software which, however, is incompatible across inputs from different technologies. Here, we present spacemake, a modular, robust and scalable spatial transcriptomics pipeline built in snakemake and python. Spacemake is designed to handle all major spatial transcriptomics datasets and can be readily configured to run on other technologies. It can process and analyze several samples in parallel, even if they stem from different experimental methods. Spacemake's unified framework enables reproducible data processing from raw sequencing data to automatically generated downstream analysis reports. Moreover, spacemake is built with a modular design and offers additional functionality such as sample merging, saturation analysis and analysis of long-reads as separate modules. Moreover, spacemake employs novoSpaRc to integrate spatial and single-cell transcriptomics data, resulting in increased gene counts for the spatial dataset. Spacemake is open-source, extendable and can be readily integrated with existing computational workflows.

scholarly journals Rapid Mycobacterium tuberculosis spoligotyping from uncorrected long reads using Galru

2020 ◽  
Author(s):  
Andrew J. Page ◽  
Nabil-Fareed Alikhan ◽  
Michael Strinden ◽  
Thanh Le Viet ◽  
Timofey Skvortsov
Keyword(s):  
Mycobacterium Tuberculosis ◽  
State Of The Art ◽  
Sequence Data ◽  
Human Pathogen ◽  
Sequencing Data ◽  
Short Read ◽  
Short Read Sequencing ◽  
Long Reads ◽  
Long Read

AbstractSpoligotyping of Mycobacterium tuberculosis provides a subspecies classification of this major human pathogen. Spoligotypes can be predicted from short read genome sequencing data; however, no methods exist for long read sequence data such as from Nanopore or PacBio. We present a novel software package Galru, which can rapidly detect the spoligotype of a Mycobacterium tuberculosis sample from as little as a single uncorrected long read. It allows for near real-time spoligotyping from long read data as it is being sequenced, giving rapid sample typing. We compare it to the existing state of the art software and find it performs identically to the results obtained from short read sequencing data. Galru is freely available from https://github.com/quadram-institute-bioscience/galru under the GPLv3 open source licence.

EdClust: A heuristic sequence clustering method with higher sensitivity

2021 ◽  
Author(s):  
Ming Cao ◽  
Qinke Peng ◽  
Ze-Gang Wei ◽  
Fei Liu ◽  
Yi-Fan Hou
Keyword(s):  
Large Scale ◽  
Sequence Data ◽  
Clustering Algorithms ◽  
Clustering Methods ◽  
Sequencing Data ◽  
Clustering Method ◽  
Cluster Number ◽  
Sequence Clustering ◽  
Downstream Analysis ◽  
Heuristic Clustering

The development of high-throughput technologies has produced increasing amounts of sequence data and an increasing need for efficient clustering algorithms that can process massive volumes of sequencing data for downstream analysis. Heuristic clustering methods are widely applied for sequence clustering because of their low computational complexity. Although numerous heuristic clustering methods have been developed, they suffer from two limitations: overestimation of inferred clusters and low clustering sensitivity. To address these issues, we present a new sequence clustering method (edClust) based on Edlib, a C/C[Formula: see text] library for fast, exact semi-global sequence alignment to group similar sequences. The new method edClust was tested on three large-scale sequence databases, and we compared edClust to several classic heuristic clustering methods, such as UCLUST, CD-HIT, and VSEARCH. Evaluations based on the metrics of cluster number and seed sensitivity (SS) demonstrate that edClust can produce fewer clusters than other methods and that its SS is higher than that of other methods. The source codes of edClust are available from https://github.com/zhang134/EdClust.git under the GNU GPL license.

scholarly journals PyGNA: a unified framework for geneset network analysis

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Viola Fanfani ◽  
Fabio Cassano ◽  
Giovanni Stracquadanio
Keyword(s):  
Data Analysis ◽  
Network Analysis ◽  
Biological Networks ◽  
High Performance ◽  
Large Scale ◽  
Sequencing Data ◽  
Unified Framework ◽  
Null Distributions ◽  
Viable Approach ◽  
Easy Integration

Abstract Background Gene and protein interaction experiments provide unique opportunities to study the molecular wiring of a cell. Integrating high-throughput functional genomics data with this information can help identifying networks associated with complex diseases and phenotypes. Results Here we introduce an integrated statistical framework to test network properties of single and multiple genesets under different interaction models. We implemented this framework as an open-source software, called Python Geneset Network Analysis (PyGNA). Our software is designed for easy integration into existing analysis pipelines and to generate high quality figures and reports. We also developed PyGNA to take advantage of multi-core systems to generate calibrated null distributions on large datasets. We then present the results of extensive benchmarking of the tests implemented in PyGNA and a use case inspired by RNA sequencing data analysis, showing how PyGNA can be easily integrated to study biological networks. PyGNA is available at http://github.com/stracquadaniolab/pygna and can be easily installed using the PyPi or Anaconda package managers, and Docker. Conclusions We present a tool for network-aware geneset analysis. PyGNA can either be readily used and easily integrated into existing high-performance data analysis pipelines or as a Python package to implement new tests and analyses. With the increasing availability of population-scale omic data, PyGNA provides a viable approach for large scale geneset network analysis.

scholarly journals LRSDAY: Long-read Sequencing Data Analysis for Yeasts

2017 ◽  
Author(s):  
Jia-Xing Yue ◽  
Gianni Liti
Keyword(s):  
Genome Assembly ◽  
Model Organism ◽  
Sequencing Data ◽  
Protein Coding ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Long Read ◽  
Downstream Analysis ◽  
Eukaryotic Organisms ◽  
Genomic Regions

AbstractLong-read sequencing technologies have become increasingly popular in genome projects due to their strengths in resolving complex genomic regions. As a leading model organism with small genome size and great biotechnological importance, the budding yeast, Saccharomyces cerevisiae, has many isolates currently being sequenced with long reads. However, analyzing long-read sequencing data to produce high-quality genome assembly and annotation remains challenging. Here we present LRSDAY, the first one-stop solution to streamline this process. LRSDAY can produce chromosome-level end-to-end genome assembly and comprehensive annotations for various genomic features (including centromeres, protein-coding genes, tRNAs, transposable elements and telomere-associated elements) that are ready for downstream analysis. Although tailored for S. cerevisiae, we designed LRSDAY to be highly modular and customizable, making it adaptable for virtually any eukaryotic organisms. Applying LRSDAY to a S. cerevisiae strain takes ∼43 hrs to generate a complete and well-annotated genome from ∼100X Pacific Biosciences (PacBio) reads using four threads.

scholarly journals Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex

2020 ◽  
Author(s):  
Kristen R. Maynard ◽  
Leonardo Collado-Torres ◽  
Lukas M. Weber ◽  
Cedric Uytingco ◽  
Brianna K. Barry ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prefrontal Cortex ◽  
Web Application ◽  
Dorsolateral Prefrontal Cortex ◽  
Large Scale ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Sequencing Data ◽  
Dorsolateral Prefrontal ◽  
Transcriptomics Data

AbstractWe used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex (DLPFC). We identified extensive layer-enriched expression signatures, and refined associations to previous laminar markers. We overlaid our laminar expression signatures onto large-scale single nuclei RNA sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially-defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions where morphological architecture is not as well-defined as cortical laminae. We lastly created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research (http://research.libd.org/spatialLIBD).

scholarly journals Discriminative Deep Hashing for Scalable Face Image Retrieval

2017 ◽  
Author(s):  
Jie Lin ◽  
Zechao Li ◽  
Jinhui Tang
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
State Of The Art ◽  
Face Image ◽  
Superior Performance ◽  
Prediction Errors ◽  
Unified Framework ◽  
Multi Scale ◽  
Deep Hashing ◽  
Hash Codes

With the explosive growth of images containing faces, scalable face image retrieval has attracted increasing attention. Due to the amazing effectiveness, deep hashing has become a popular hashing method recently. In this work, we propose a new Discriminative Deep Hashing (DDH) network to learn discriminative and compact hash codes for large-scale face image retrieval. The proposed network incorporates the end-to-end learning, the divide-and-encode module and the desired discrete code learning into a unified framework. Specifically, a network with a stack of convolution-pooling layers is proposed to extract multi-scale and robust features by merging the outputs of the third max pooling layer and the fourth convolutional layer. To reduce the redundancy among hash codes and the network parameters simultaneously, a divide-and-encode module to generate compact hash codes. Moreover, a loss function is introduced to minimize the prediction errors of the learned hash codes, which can lead to discriminative hash codes. Extensive experiments on two datasets demonstrate that the proposed method achieves superior performance compared with some state-of-the-art hashing methods.

scholarly journals Characterization and simulation of metagenomic nanopore sequencing data with Meta-NanoSim

2021 ◽  
Author(s):  
Chen Yang ◽  
Theodora Lo ◽  
Ka Ming Nip ◽  
Saber Hafezqorani ◽  
Rene L Warren ◽  
...  
Keyword(s):  
State Of The Art ◽  
Simulated Data ◽  
Nanopore Sequencing ◽  
Sequencing Data ◽  
Base Level ◽  
Long Reads ◽  
Structural Differences ◽  
High Base ◽  
Metagenomic Assembly ◽  
Analytical Tools

Nanopore sequencing is crucial to metagenomic studies as its kilobase-long reads can contribute to resolving genomic structural differences among microbes. However, platform-specific challenges, including high base-call error rate, non-uniform read lengths, and the presence of chimeric artifacts, necessitate specifically designed analytical tools. Here, we present Meta-NanoSim, a fast and versatile utility that characterizes and simulates the unique properties of nanopore metagenomic reads. Further, Meta-NanoSim improves upon state-of-the-art methods on microbial abundance estimation through a base-level quantification algorithm. We demonstrate that Meta-NanoSim simulated data can facilitate the development of metagenomic algorithms and guide experimental design through a metagenomic assembly benchmarking task.

scholarly journals Clustering de Novo by Gene of Long Reads from Transcriptomics Data

2017 ◽  
Author(s):  
Camille Marchet ◽  
Lolita Lecompte ◽  
Corinne Da Silva ◽  
Corinne Cruaud ◽  
Jean-Marc Aury ◽  
...  
Keyword(s):  
De Novo ◽  
Free Access ◽  
Sequencing Data ◽  
Base Pairs ◽  
Long Reads ◽  
Oxford Nanopore ◽  
Processing Step ◽  
Whole Transcriptome Sequencing ◽  
Long Read ◽  
Transcriptomics Data

AbstractLong-read sequencing currently provides sequences of several thousand base pairs. This allows to obtain complete transcripts, which offers an un-precedented vision of the cellular transcriptome.However the literature is lacking tools to cluster such data de novo, in particular for Oxford Nanopore Technologies reads, because of the inherent high error rate compared to short reads.Our goal is to process reads from whole transcriptome sequencing data accurately and without a reference genome in order to reliably group reads coming from the same gene. This de novo approach is therefore particularly suitable for non-model species, but can also serve as a useful pre-processing step to improve read mapping. Our contribution is both to propose a new algorithm adapted to clustering of reads by gene and a practical and free access tool that permits to scale the complete processing of eukaryotic transcriptomes.We sequenced a mouse RNA sample using the MinION device, this dataset is used to compare our solution to other algorithms used in the context of biological clustering. We demonstrate its is better-suited for transcriptomics long reads. When a reference is available thus mapping possible, we show that it stands as an alternative method that predicts complementary clusters.

scholarly journals ELECTOR: Evaluator for long reads correction methods

2019 ◽  
Author(s):  
Camille Marchet ◽  
Pierre Morisse ◽  
Lolita Lecompte ◽  
Arnaud Lefebvre ◽  
Thierry Lecroq ◽  
...  
Keyword(s):  
Error Correction ◽  
State Of The Art ◽  
Error Rates ◽  
Sequencing Data ◽  
Third Generation Sequencing ◽  
Long Reads ◽  
Wide Range ◽  
Downstream Processes ◽  
Generation Sequencing

AbstractMotivationIn the last few years, the error rates of third generation sequencing data have been capped above 5%, including many insertions and deletions. Thereby, an increasing number of long reads correction methods have been proposed to reduce the noise in these sequences. Whether hybrid or self-correction methods, there exist multiple approaches to correct long reads. As the quality of the error correction has huge impacts on downstream processes, developing methods allowing to evaluate error correction tools with precise and reliable statistics is therefore a crucial need. Since error correction is often a resource bottleneck in long reads pipelines, a key feature of assessment methods is therefore to be efficient, in order to allow the fast comparison of different tools.ResultsWe propose ELECTOR, a reliable and efficient tool to evaluate long reads correction, that enables the evaluation of hybrid and self-correction methods. Our tool provides a complete and relevant set of metrics to assess the read quality improvement after correction and scales to large datasets. ELECTOR is directly compatible with a wide range of state-of-the-art error correction tools, using whether simulated or real long reads. We show that ELECTOR displays a wider range of metrics than the state-of-the-art tool, LRCstats, and additionally importantly decreases the runtime needed for assessment on all the studied datasets.AvailabilityELECTOR is available at https://github.com/kamimrcht/[email protected] or [email protected]

scholarly journals High-quality carnivore genomes from roadkill samples enable species delimitation in aardwolf and bat-eared fox

2020 ◽  
Author(s):  
Rémi Allio ◽  
Marie-Ka Tilak ◽  
Céline Scornavacca ◽  
Nico L. Avenant ◽  
Erwan Corre ◽  
...  
Keyword(s):  
Large Scale ◽  
Incomplete Lineage Sorting ◽  
Cost Effective ◽  
Genomic Diversity ◽  
Sequencing Data ◽  
Lineage Sorting ◽  
Long Reads ◽  
Genomic Studies ◽  
Eastern And Southern Africa ◽  
Reference Genomes

AbstractIn a context of ongoing biodiversity erosion, obtaining genomic resources from wildlife is becoming essential for conservation. The thousands of yearly mammalian roadkill could potentially provide a useful source material for genomic surveys. To illustrate the potential of this underexploited resource, we used roadkill samples to sequence reference genomes and study the genomic diversity of the bat-eared fox (Otocyon megalotis) and the aardwolf (Proteles cristata) for which subspecies have been defined based on similar disjunct distributions in Eastern and Southern Africa. By developing an optimized DNA extraction protocol, we successfully obtained long reads using the Oxford Nanopore Technologies (ONT) MinION device. For the first time in mammals, we obtained two reference genomes with high contiguity and gene completeness by combining ONT long reads with Illumina short reads using hybrid assembly. Based on re-sequencing data from few other roakill samples, the comparison of the genetic differentiation between our two pairs of subspecies to that of pairs of well-defined species across Carnivora showed that the two subspecies of aardwolf might warrant species status (P. cristata and P. septentrionalis), whereas the two subspecies of bat-eared fox might not. Moreover, using these data, we conducted demographic analyses that revealed similar trajectories between Eastern and Southern populations of both species, suggesting that their population sizes have been shaped by similar environmental fluctuations. Finally, we obtained a well resolved genome-scale phylogeny for Carnivora with evidence for incomplete lineage sorting among the three main arctoid lineages. Overall, our cost-effective strategy opens the way for large-scale population genomic studies and phylogenomics of mammalian wildlife using roadkill.

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