scholarly journals Leveraging multiple transcriptome assembly methods for improved gene structure annotation

2017 ◽  
Author(s):  
Luca Venturini ◽  
Shabhonam Caim ◽  
Gemy G Kaithakottil ◽  
Daniel L Mapleson ◽  
David Swarbreck
Keyword(s):  
Open Source ◽  
Gene Structure ◽  
Transcriptome Assembly ◽  
Rna Seq ◽  
Multiple Methods ◽  
Link Type ◽  
Transcript Reconstruction ◽  
Structure Annotation ◽  
Optimal Approach ◽  
Novel Algorithm

AbstractThe performance of RNA-Seq aligners and assemblers varies greatly across different organisms and experiments, and often the optimal approach is not known beforehand. Here we show that the accuracy of transcript reconstruction can be boosted by combining multiple methods, and we present a novel algorithm to integrate multiple RNA-Seq assemblies into a coherent transcript annotation. Our algorithm can remove redundancies and select the best transcript models according to user-specified metrics, while solving common artefacts such as erroneous transcript chimerisms. We have implemented this method in an open-source Python3 and Cython program, Mikado, available at https://github.com/lucventurini/Mikado.

scholarly journals NASQAR: A web-based platform for high-throughput sequencing data analysis and visualization

2019 ◽  
Author(s):  
Ayman Yousif ◽  
Nizar Drou ◽  
Jillian Rowe ◽  
Mohammed Khalfan ◽  
Kristin C Gunsalus
Keyword(s):  
New York ◽  
Data Analysis ◽  
Open Source ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Web Applications ◽  
Rna Seq ◽  
Sequencing Data ◽  
Web Based ◽  
Link Type

AbstractBackgroundAs high-throughput sequencing applications continue to evolve, the rapid growth in quantity and variety of sequence-based data calls for the development of new software libraries and tools for data analysis and visualization. Often, effective use of these tools requires computational skills beyond those of many researchers. To ease this computational barrier, we have created a dynamic web-based platform, NASQAR (Nucleic Acid SeQuence Analysis Resource).ResultsNASQAR offers a collection of custom and publicly available open-source web applications that make extensive use of a variety of R packages to provide interactive data analysis and visualization. The platform is publicly accessible at http://nasqar.abudhabi.nyu.edu/. Open-source code is on GitHub at https://github.com/nasqar/NASQAR, and the system is also available as a Docker image at https://hub.docker.com/r/aymanm/nasqarall. NASQAR is a collaboration between the core bioinformatics teams of the NYU Abu Dhabi and NYU New York Centers for Genomics and Systems Biology.ConclusionsNASQAR empowers non-programming experts with a versatile and intuitive toolbox to easily and efficiently explore, analyze, and visualize their Transcriptomics data interactively. Popular tools for a variety of applications are currently available, including Transcriptome Data Preprocessing, RNA-seq Analysis (including Single-cell RNA-seq), Metagenomics, and Gene Enrichment.

scholarly journals Comparative De Novo Transcriptome Assembly of Notophthalmus viridescens RNA-seq Data using Two Commercial Software Programs

2018 ◽  
Vol 16 (1) ◽  
pp. 46-53
Author(s):  
Jonathan Chacon ◽  
Math P. Cuajungco
Keyword(s):  
Data Analysis ◽  
Open Source ◽  
De Novo ◽  
Transcriptome Assembly ◽  
Model Organism ◽  
Rna Seq ◽  
Basic Algorithm ◽  
Commercial Software ◽  
Data Output ◽  
Software Programs

Background and Purpose: The reduction of cost and ease of using core laboratories or commercial sequencing companies have allowed biomedical and health researchers alike to employ reference-based genomic or transcriptomic sequencing (RNA-seq) projects to expand their work. Non-reference based data analysis, in cases of inexperienced researchers, become more challenging despite the availability of many open source and commercial software programs. Methods: We performed de novo assembly of RNA-seq data obtained from a non-model organism (Eastern Newt skin) to compare data output of two commercially available software workflows. Results: Our results show that the software packages performed satisfactorily albeit with differences in how the annotated and novel transcripts were identified and listed. Conclusion: Overall, we conclude that the use of commercial software platforms has a clear advantage to that of open source programs because of convenience with data analysis workflows. One caveat is that users need to know the software’s basic algorithm and technical approach, in order to determine the precision and validity of the data output. Thus, it is imperative that researchers fully evaluate the software according to their needs to determine their suitability.

scholarly journals Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database

2017 ◽  
Author(s):  
Luke Zappia ◽  
Belinda Phipson ◽  
Alicia Oshlack
Keyword(s):  
Single Cell ◽  
Open Source ◽  
Rapid Development ◽  
Analysis Tool ◽  
Rna Seq ◽  
Link Type ◽  
Analysis Tools ◽  
Rapid Pace ◽  
Cell Data ◽  
Selection Of

AbstractAs single-cell RNA-sequencing (scRNA-seq) datasets have become more widespread the number of tools designed to analyse these data has dramatically increased. Navigating the vast sea of tools now available is becoming increasingly challenging for researchers. In order to better facilitate selection of appropriate analysis tools we have created the scRNA-tools database (www.scRNA-tools.org) to catalogue and curate analysis tools as they become available. Our database collects a range of information on each scRNA-seq analysis tool and categorises them according to the analysis tasks they perform. Exploration of this database gives insights into the areas of rapid development of analysis methods for scRNA-seq data. We see that many tools perform tasks specific to scRNA-seq analysis, particularly clustering and ordering of cells. We also find that the scRNA-seq community embraces an open-source approach, with most tools available under open-source licenses and preprints being extensively used as a means to describe methods. The scRNA-tools database provides a valuable resource for researchers embarking on scRNA-seq analysis and records of the growth of the field over time.Author summaryIn recent years single-cell RNA-sequeing technologies have emerged that allow scientists to measure the activity of genes in thousands of individual cells simultaneously. This means we can start to look at what each cell in a sample is doing instead of considering an average across all cells in a sample, as was the case with older technologies. However, while access to this kind of data presents a wealth of opportunities it comes with a new set of challenges. Researchers across the world have developed new methods and software tools to make the most of these datasets but the field is moving at such a rapid pace it is difficult to keep up with what is currently available. To make this easier we have developed the scRNA-tools database and website (www.scRNA-tools.org). Our database catalogues analysis tools, recording the tasks they can be used for, where they can be downloaded from and the publications that describe how they work. By looking at this database we can see that developers have focued on methods specific to single-cell data and that they embrace an open-source approach with permissive licensing, sharing of code and preprint publications.

scholarly journals ABRIDGE: An ultra-compression software for SAM alignment files

2022 ◽  
Author(s):  
Sagnik Banerjee ◽  
Carson Andorf
Keyword(s):  
Compression Ratio ◽  
State Of The Art ◽  
Transcriptome Assembly ◽  
Genetic Data ◽  
Redundant Information ◽  
Rna Seq ◽  
New Approach ◽  
Gene Count ◽  
Lossless And Lossy Compression ◽  
Novel Algorithm

Advancement in technology has enabled sequencing machines to produce vast amounts of genetic data, causing an increase in storage demands. Most genomic software utilizes read alignments for several purposes including transcriptome assembly and gene count estimation. Herein we present, ABRIDGE, a state-of-the-art compressor for SAM alignment files offering users both lossless and lossy compression options. This reference-based file compressor achieves the best compression ratio among all compression software ensuring lower space demand and faster file transmission. Central to the software is a novel algorithm that retains non-redundant information. This new approach has allowed ABRIDGE to achieve a compression 16% higher than the second-best compressor for RNA-Seq reads and over 35% for DNA-Seq reads. ABRIDGE also offers users the option to randomly access location without having to decompress the entire file. ABRIDGE is distributed under MIT license and can be obtained from GitHub and docker hub. We anticipate that the user community will adopt ABRIDGE within their existing pipeline encouraging further research in this domain.

scholarly journals RNA-seq, de novo transcriptome assembly and flavonoid gene analysis in 13 wild and cultivated berry fruit species with high content of phenolics

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Vera Thole ◽  
Jean-Etienne Bassard ◽  
Ricardo Ramírez-González ◽  
Martin Trick ◽  
Bijan Ghasemi Afshar ◽  
...  
Keyword(s):  
Biosynthetic Pathway ◽  
De Novo ◽  
Transcriptome Assembly ◽  
Rubus Idaeus ◽  
Wild Plant ◽  
Anthocyanin Synthesis ◽  
Rna Seq ◽  
Link Type ◽  
Fruit Species ◽  
Wide Range

Abstract Background Flavonoids are produced in all flowering plants in a wide range of tissues including in berry fruits. These compounds are of considerable interest for their biological activities, health benefits and potential pharmacological applications. However, transcriptomic and genomic resources for wild and cultivated berry fruit species are often limited, despite their value in underpinning the in-depth study of metabolic pathways, fruit ripening as well as in the identification of genotypes rich in bioactive compounds. Results To access the genetic diversity of wild and cultivated berry fruit species that accumulate high levels of phenolic compounds in their fleshy berry(-like) fruits, we selected 13 species from Europe, South America and Asia representing eight genera, seven families and seven orders within three clades of the kingdom Plantae. RNA from either ripe fruits (ten species) or three ripening stages (two species) as well as leaf RNA (one species) were used to construct, assemble and analyse de novo transcriptomes. The transcriptome sequences are deposited in the BacHBerryGEN database (http://jicbio.nbi.ac.uk/berries) and were used, as a proof of concept, via its BLAST portal (http://jicbio.nbi.ac.uk/berries/blast.html) to identify candidate genes involved in the biosynthesis of phenylpropanoid compounds. Genes encoding regulatory proteins of the anthocyanin biosynthetic pathway (MYB and basic helix-loop-helix (bHLH) transcription factors and WD40 repeat proteins) were isolated using the transcriptomic resources of wild blackberry (Rubus genevieri) and cultivated red raspberry (Rubus idaeus cv. Prestige) and were shown to activate anthocyanin synthesis in Nicotiana benthamiana. Expression patterns of candidate flavonoid gene transcripts were also studied across three fruit developmental stages via the BacHBerryEXP gene expression browser (http://www.bachberryexp.com) in R. genevieri and R. idaeus cv. Prestige. Conclusions We report a transcriptome resource that includes data for a wide range of berry(-like) fruit species that has been developed for gene identification and functional analysis to assist in berry fruit improvement. These resources will enable investigations of metabolic processes in berries beyond the phenylpropanoid biosynthetic pathway analysed in this study. The RNA-seq data will be useful for studies of berry fruit development and to select wild plant species useful for plant breeding purposes.

scholarly journals Leveraging multiple transcriptome assembly methods for improved gene structure annotation

GigaScience ◽  
2018 ◽  
Vol 7 (8) ◽  
Author(s):  
Luca Venturini ◽  
Shabhonam Caim ◽  
Gemy George Kaithakottil ◽  
Daniel Lee Mapleson ◽  
David Swarbreck
Keyword(s):  
Gene Structure ◽  
Transcriptome Assembly ◽  
Structure Annotation

scholarly journals S-IRFindeR: stable and accurate measurement of intron retention

2020 ◽  
Author(s):  
Lucile Broseus ◽  
William Ritchie
Keyword(s):  
Intron Retention ◽  
Rna Seq ◽  
Sequencing Data ◽  
New Approach ◽  
Retention Ratio ◽  
Link Type ◽  
Long Read ◽  
Retained Introns ◽  
Accurate Quantification ◽  
Novel Algorithm

AbstractAccurate quantification of intron retention levels is currently the crux for detecting and interpreting the function of retained introns. Using both simulated and real RNA-seq datasets, we show that current methods suffer from several biases and artefacts, which impair the analysis of intron retention. We designed a new approach to measure intron retention levels called the Stable Intron Retention ratio that we have implemented in a novel algorithm to detect and measure intron retention called S-IRFindeR. We demonstrate that it provides a significant improvement in accuracy, higher consistency between replicates and agreement with IR-levels computed from long-read sequencing data.S-IRFindeR is freely available at: https://github.com/lbroseus/SIRFindeR/.

scholarly journals Boiler: Lossy compression of RNA-seq alignments using coverage vectors

2016 ◽  
Author(s):  
Jacob Pritt ◽  
Ben Langmead
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Negative Impact ◽  
Software Tool ◽  
Lossy Compression ◽  
Rna Seq ◽  
Empirical Distributions ◽  
Free Open Source Software ◽  
Link Type ◽  
Free Open Source

AbstractWe describe Boiler, a new software tool for compressing and querying large collections of RNA-seq alignments. Boiler discards most per-read data, keeping only a genomic coverage vector plus a few empirical distributions summarizing the alignments. Since most per-read data is discarded, storage footprint is often much smaller than that achieved by other compression tools. Despite this, the most relevant per-read data can be recovered; we show that Boiler compression has only a slight negative impact on results given by downstream tools for isoform assembly and quantification. Boiler also allows the user to pose fast and useful queries without decompressing the entire file. Boiler is free open source software available from github.com/jpritt/boiler.

scholarly journals snakePipes enable flexible, scalable and integrative epigenomic analysis

2018 ◽  
Author(s):  
Vivek Bhardwaj ◽  
Steffen Heyne ◽  
Katarzyna Sikora ◽  
Leily Rabbani ◽  
Michael Rauer ◽  
...  
Keyword(s):  
Single Cell ◽  
Open Source ◽  
Large Scale ◽  
Integrative Analysis ◽  
Exploratory Research ◽  
Rna Seq ◽  
Link Type ◽  
Open Source License ◽  
Fast Processing ◽  
Downstream Analysis

AbstractThe scale and diversity of epigenomics data has been rapidly increasing and ever more studies now present analyses of data from multiple epigenomic techniques. Performing such integrative analysis is time-consuming, especially for exploratory research, since there are currently no pipelines available that allow fast processing of datasets from multiple epigenomic assays while also allow for flexibility in running or upgrading the workflows. Here we present a solution to this problem: snakePipes, which can process and perform downstream analysis of data from all common epigenomic techniques (ChIP-seq, RNA-seq, Bisulfite-seq, ATAC-seq, Hi-C and single-cell RNA-seq) in a single package. We demonstrate how snakePipes can simplify integrative analysis by reproducing and extending the results from a recently published large-scale epigenomics study with a few simple commands. snakePipes are available under an open-source license at https://github.com/maxplanck-ie/snakepipes.

scholarly journals RP-REP Ribosomal Profiling Reports: an open-source cloud-enabled framework for reproducible ribosomal profiling data processing, analysis, and result reporting

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 143
Author(s):  
Travis L. Jensen ◽  
William F. Hooper ◽  
Sami R. Cherikh ◽  
Johannes B. Goll
Keyword(s):  
Protein Synthesis ◽  
Open Source ◽  
Biomarker Discovery ◽  
Reference Alignment ◽  
Translational Efficiency ◽  
Rna Seq ◽  
Link Type ◽  
Huh7 Cells ◽  
Study Results ◽  
The Impact

Ribosomal profiling is an emerging experimental technology to measure protein synthesis by sequencing short mRNA fragments undergoing translation in ribosomes. Applied on the genome wide scale, this is a powerful tool to profile global protein synthesis within cell populations of interest. Such information can be utilized for biomarker discovery and detection of treatment-responsive genes. However, analysis of ribosomal profiling data requires careful preprocessing to reduce the impact of artifacts and dedicated statistical methods for visualizing and modeling the high-dimensional discrete read count data. Here we present Ribosomal Profiling Reports (RP-REP), a new open-source cloud-enabled software that allows users to execute start-to-end gene-level ribosomal profiling and RNA-Seq analysis on a pre-configured Amazon Virtual Machine Image (AMI) hosted on AWS or on the user’s own Ubuntu Linux server. The software works with FASTQ files stored locally, on AWS S3, or at the Sequence Read Archive (SRA). RP-REP automatically executes a series of customizable steps including filtering of contaminant RNA, enrichment of true ribosomal footprints, reference alignment and gene translation quantification, gene body coverage, CRAM compression, reference alignment QC, data normalization, multivariate data visualization, identification of differentially translated genes, and generation of heatmaps, co-translated gene clusters, enriched pathways, and other custom visualizations. RP-REP provides functionality to contrast RNA-SEQ and ribosomal profiling results, and calculates translational efficiency per gene. The software outputs a PDF report and publication-ready table and figure files. As a use case, we provide RP-REP results for a dengue virus study that tested cytosol and endoplasmic reticulum cellular fractions of human Huh7 cells pre-infection and at 6 h, 12 h, 24 h, and 40 h post-infection. Case study results, Ubuntu installation scripts, and the most recent RP-REP source code are accessible at GitHub. The cloud-ready AMI is available at AWS (AMI ID: RPREP RSEQREP (Ribosome Profiling and RNA-Seq Reports) v2.1 (ami-00b92f52d763145d3)).

Sign in / Sign up

Export Citation Format

Share Document