ARMT: An automatic RNA-seq data mining tool based on comprehensive and integrative analysis in cancer research.
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ABSTRACT:
SUBMITTER: Huang G
PROVIDER: S-EPMC8379379 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Gene expression in individual cells can now be measured for thousands of cells in a single experiment thanks to innovative sample-preparation and sequencing technologies. State-of-the-art computational pipelines for single-cell RNA-sequencing data, however, still employ computational methods that were developed for traditional bulk RNA-sequencing data, thus not accounting for the peculiarities of single-cell data, such as sparseness and zero-inflated counts. Here, we present a ready-to-use pipeline named gf-icf (gene frequency-inverse cell frequency) for normalization of raw counts, feature selection, and dimensionality reduction of scRNA-seq data for their visualization and subsequent analyses. Our work is based on a data transformation model named term frequency-inverse document frequency (TF-IDF), which has been extensively used in the field of text mining where extremely sparse and zero-inflated data are common. Using benchmark scRNA-seq datasets, we show that the gf-icf pipeline outperforms existing state-of-the-art methods in terms of improved visualization and ability to separate and distinguish different cell types.
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Project description:RNA sequencing (RNA-seq) provides novel opportunities for transcriptomic studies at nucleotide resolution, including transcriptomics of viruses or microbes infecting a cell. However, standard approaches for mapping the resulting sequencing reads generally ignore alternative sources of expression other than the host cell and are little equipped to address the problems arising from redundancies and gaps among sequenced microbe and virus genomes. We show that screening of sequencing reads for contaminations and infections can be performed easily using ContextMap, our recently developed mapping software. Based on mapping-derived statistics, mapping confidence, similarities and misidentifications (e.g. due to missing genome sequences) of species/strains can be assessed. Performance of our approach is evaluated on three real-life sequencing data sets and compared to state-of-the-art metagenomics tools. In particular, ContextMap vastly outperformed GASiC and GRAMMy in terms of runtime. In contrast to MEGAN4, it was capable of providing individual read mappings to species and resolving non-unique mappings, thus allowing the identification of misalignments caused by sequence similarities between genomes and missing genome sequences. Our study illustrates the importance and potentials of routinely mining RNA-seq experiments for infections or contaminations by microbes and viruses. By using ContextMap, gene expression of infecting agents can be analyzed and novel insights in infection processes and tumorigenesis can be obtained.
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Project description:Small non-coding RNAs have been significantly recognized as the key modulators in many biological processes, and are emerging as promising biomarkers for several diseases. These RNA species are transcribed in cells and can be packaged in extracellular vesicles, which are small vesicles released from many biotypes, and are involved in intercellular communication. Currently, the advent of next-generation sequencing (NGS) technology for high-throughput profiling has further advanced the biological insights of non-coding RNA on a genome-wide scale and has become the preferred approach for the discovery and quantification of non-coding RNA species. Despite the routine practice of NGS, the processing of large data sets poses difficulty for analysis before conducting downstream experiments. Often, the current analysis tools are designed for specific RNA species, such as microRNA, and are limited in flexibility for modifying parameters for optimization. An analysis tool that allows for maximum control of different software is essential for drawing concrete conclusions for differentially expressed transcripts. Here, we developed a one-touch integrated small RNA analysis pipeline (iSRAP) research tool that is composed of widely used tools for rapid profiling of small RNAs. The performance test of iSRAP using publicly and in-house available data sets shows its ability of comprehensive profiling of small RNAs of various classes, and analysis of differentially expressed small RNAs. iSRAP offers comprehensive analysis of small RNA sequencing data that leverage informed decisions on the downstream analyses of small RNA studies, including extracellular vesicles such as exosomes.
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Project description:BackgroundTranscriptome sequencing is a powerful tool for measuring gene expression, but as well as some other technologies, various artifacts and biases affect the quantification. In order to correct some of them, several normalization approaches have emerged, differing both in the statistical strategy employed and in the type of corrected biases. However, there is no clear standard normalization method.ResultsWe present a novel methodology to normalize RNA-Seq data, taking into account transcript size, GC content, and sequencing depth, which are the major quantification-related biases. In this study, we found that transcripts shorter than 600 bp have an underestimated expression level, while longer transcripts are even more overestimated that they are long. Second, it was well known that the higher the GC content (>50%), the more the transcripts are underestimated. Third, we demonstrated that the sequencing depth impacts the size bias and proposed a correction allowing the comparison of expression levels among many samples. The efficiency of our approach was then tested by comparing the correlation between normalized RNA-Seq data and qRT-PCR expression measurements. All the steps are automated in a program written in Perl and available on request.ConclusionsThe methodology presented in this article identifies and corrects different biases that influence RNA-Seq quantification, and provides more accurate estimations of gene expression levels. This method can be applied to compare expression quantifications from many samples, but preferentially from the same tissue. In order to compare samples from different tissue, a calibration using several reference genes will be required.
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Project description:RNA sequencing (RNA-seq) is the leading technology for genome-wide transcript quantification. However, publicly available RNA-seq data is currently provided mostly in raw form, a significant barrier for global and integrative retrospective analyses. ARCHS4 is a web resource that makes the majority of published RNA-seq data from human and mouse available at the gene and transcript levels. For developing ARCHS4, available FASTQ files from RNA-seq experiments from the Gene Expression Omnibus (GEO) were aligned using a cloud-based infrastructure. In total 187,946 samples are accessible through ARCHS4 with 103,083 mouse and 84,863 human. Additionally, the ARCHS4 web interface provides intuitive exploration of the processed data through querying tools, interactive visualization, and gene pages that provide average expression across cell lines and tissues, top co-expressed genes for each gene, and predicted biological functions and protein-protein interactions for each gene based on prior knowledge combined with co-expression.
| S-EPMC5893633 | biostudies-other
Project description:The current DNA sequencing technologies and their high-throughput yield, allowed the thrive of genomic and transcriptomic experiments but it also have generated big data problem. Due to this exponential growth of sequencing data, also the complexity of managing, processing and interpreting it in order to generate results, has raised. Therefore, the demand of easy-to-use friendly software and websites to run bioinformatic tools is imminent. In particular, RNA-Seq and differential expression analysis have become a popular and useful method to evaluate the genetic expression change in any organism. However, many scientists struggle with the data analysis since most of the available tools are implemented in a UNIX-based environment. Therefore, we have developed the web server IDEAMEX (Integrative Differential Expression Analysis for Multiple EXperiments). The IDEAMEX pipeline needs a raw count table for as many desired replicates and conditions, allowing the user to select which conditions will be compared, instead of doing all-vs.-all comparisons. The whole process consists of three main steps (1) Data Analysis: that allows a preliminary analysis for quality control based on the data distribution per sample, using different types of graphs; (2) Differential expression: performs the differential expression analysis with or without batch effect error awareness, using the bioconductor packages, NOISeq, limma-Voom, DESeq2 and edgeR, and generate reports for each method; (3) Result integration: the obtained results the integrated results are reported using different graphical outputs such as correlograms, heatmaps, Venn diagrams and text lists. Our server allows an easy and friendly visualization for results, providing an easy interaction during the analysis process, as well as error tracking and debugging by providing output log files. The server is currently available and can be accessed at http://www.uusmb.unam.mx/ideamex/ where the documentation and example input files are provided. We consider that this web server can help other researchers with no previous bioinformatic knowledge, to perform their analyses in a simple manner.
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Project description:Long non-coding RNAs (lncRNAs) play an essential role in diverse biological processes and disease development. Accurate classification of lncRNAs and mRNAs is important for the identification of tissue- or disease-specific lncRNAs. Here, we present our tool LncDC (Long non-coding RNA detection) that is able to accurately predict lncRNAs with an XGBoost model using features extracted from RNA sequences, secondary structures, and translated proteins. Benchmarking experiments showed that LncDC consistently outperformed six state-of-the-art tools in distinguishing lncRNAs from mRNAs. Notably, the use of sequence and secondary structure (SASS) k-mer score features and flexible ORF features improved the classification capability of LncDC. We anticipate that LncDC will definitely promote the discovery of more and novel disease-specific lncRNAs. LncDC is implemented in Python and freely available at https://github.com/lim74/LncDC .
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Project description:CLIP-seq is widely used to study genome-wide interactions between RNA-binding proteins and RNAs. However, there are few tools available to analyze CLIP-seq data, thus creating a bottleneck to the implementation of this methodology. Here, we present PIPE-CLIP, a Galaxy framework-based comprehensive online pipeline for reliable analysis of data generated by three types of CLIP-seq protocol: HITS-CLIP, PAR-CLIP and iCLIP. PIPE-CLIP provides both data processing and statistical analysis to determine candidate cross-linking regions, which are comparable to those regions identified from the original studies or using existing computational tools. PIPE-CLIP is available at http://pipeclip.qbrc.org/.
| S-EPMC4054095 | biostudies-literature
Project description:BACKGROUND:The use of RNA-sequencing (RNA-seq) in molecular biology research and clinical settings has increased significantly over the past decade. Despite its widespread adoption, there is a lack of simple and interactive tools to analyze and explore RNA-seq data. Many established tools require programming or Unix/Bash knowledge to analyze and visualize results. This requirement presents a significant barrier for many researchers to efficiently analyze and present RNA-seq data. RESULTS:Here we present BEAVR, a Browser-based tool for the Exploration And Visualization of RNA-seq data. BEAVR is an easy-to-use tool that facilitates interactive analysis and exploration of RNA-seq data. BEAVR is developed in R and uses DESeq2 as its engine for differential gene expression (DGE) analysis, but assumes users have no prior knowledge of R or DESeq2. BEAVR allows researchers to easily obtain a table of differentially-expressed genes with statistical testing and then visualize the results in a series of graphs, plots and heatmaps. Users are able to customize many parameters for statistical testing, dealing with variance, clustering methods and pathway analysis to generate high quality figures. CONCLUSION:BEAVR simplifies analysis for novice users but also streamlines the RNA-seq analysis process for experts by automating several steps. BEAVR and its documentation can be found on GitHub at https://github.com/developerpiru/BEAVR. BEAVR is available as a Docker container at https://hub.docker.com/r/pirunthan/beavr.
| S-EPMC7260831 | biostudies-literature