1、小RNA工具：iSmaRT

摘要：

iSmaRT is a collection of bioinformatics tools and own algorithms, interconnected through a Graphical User Interface (GUI). In addition to performing comprehensive analyses on miRNAs, it implements specific computational modules to analyze piRNAs, predicting novel ones and identifying their RNA targets.

下载网站：

ftp://labmedmolge-1.unisa.it (User: iSmart - Password: password)

2、circRNAs工具

摘要：

Here,we present CirComPara , an automated bioinformatics pipeline, to detect, quantify and annotate circRNAs from RNAseq data using in parallel four different methods for backsplice identification. CirComPara also provides quantification of linear RNAs and gene expression, ultimately comparing and correlating circRNA and gene/transcript expression level.

网址：http://github.com/egaffo/CirComPara

3、宏转录组工具：IMSA+A

摘要：

We present a new protocol, IMSA+A, for accurate taxonomy classification based on metatranscriptome data of any read length that can efficiently and robustly identify bacteria, fungi, and viruses in the same sample. 

下载地址：

https://github.com/JeremyCoxBMI/IMSA-A.

4、RNAseq工具：Snaptron

摘要：

As more and larger genomics studies appear, there is a growing need for comprehensive and queryable cross-study summaries. Snaptron is a search engine for summarized RNA sequencing data with a query planner that leverages R-tree, B-tree and inverted indexing strategies to rapidly execute queries over 146 million exon-exon splice junctions from over 70,000 human RNA-seq samples. Queries can be tailored by constraining which junctions and samples to consider. Snaptron can also rank and score junctions according to tissue specificity or other criteria. Further, Snaptron can rank and score samples according to the relative frequency of different splicing patterns. We outline biological questions that can be explored with Snaptron queries, including a study of novel exons in annotated genes, of exonization of repetitive element loci, and of a recently discovered alternative transcription start site for the ALK gene. 

下载地址：

http://snaptron.cs.jhu.edu. 

https://github.com/ChristopherWilks/snaptron 

5、基因融合工具：ChimPipe

摘要：

Here we present ChimPipe, a modular and easy-to-use method to reliably identify fusion genes and transcription-induced chimeras from paired-end Illumina RNA-seq data.

6、综合工具包：KNIME4NGS

摘要：

Here, we present a documented, linux-based, toolbox of 42 processing modules that are combined to construct workflows facilitating a variety of tasks such as DNAseq and RNAseq analysis. We also describe important technical extensions. The high throughput executor helps to increase the reliability and to reduce manual interventions when processing complex datasets. 

下载地址：http://ibisngs.github.io/knime4ngs 

7、基因融合工具：FuGePrior

摘要：

In this work we propose a novel methodological approach and tool named FuGePrior for the prioritization of gene fusions from paired-end RNA-Seq data. The proposed pipeline combines state of the art tools for chimeric transcript discovery and prioritization, a series of filtering and processing steps designed by considering modern literature on gene fusions and an analysis on functional reliability of gene fusion structure.

下载地址：

8、差异表达工具：DEApp

摘要：

We developed DEApp, an interactive and dynamic web application for differential expression analysis of count based NGS data. This application enables models selection, parameter tuning, cross validation and visualization of results in a user-friendly interface.

下载地址： https://yanli.shinyapps.io/DEAppandhttps://gallery.shinyapps.io/DEApp.

9、共表达数据库：CORNET 3.0

摘要：

CORNET 3.0 is the third iteration of the web platform designed for the user exploration of the coexpression space of plant genomes, with a focus on the model species Arabidopsis thaliana. Here we describe the platform: the tools, data, and best practices when using the platform. We indicate how the platform can be used to infer networks from a set of input genes, such as upregulated genes from an expression experiment. By exploring the network, new target and regulator genes can be discovered, allowing for follow-up experiments and more in-depth study. 

下载网址：http://bioinformatics.psb.ugent.be/cornet/.

10、标准化工具:rnaSeqAssumptions

摘要：

Contains simulation code for evaluating RNA-Seq normalization methods when assumptions are violated. To analyze a biological experiment, researchers must select a normalization method with assumptions that are met and that produces a meaningful measure of expression for the given experiment.

下载网址：https://github.com/ciaranlevans/rnaSeqAssumptions

11、质控工具：RQC

摘要：

Allows quality control and assessment of high-throughput sequencing data. Rqc performs parallel processing of entire files and produces a report which contains a set of high-resolution graphics. It produces an HTML report, which contains a set of high-resolution images that can be directly used on publications.

下载网址：https://github.com/Bioconductor-mirror/Rqc/tree/release-3.4

参考文献

１、Panero R, Rinaldi A, Memoli D, et al. iSmaRT: a toolkit for a comprehensive analysis of small RNA-Seq data[J]. Bioinformatics, 2017: btw734.

２、Gaffo E, Bonizzato A, Kronnie G, et al. CirComPara: A Multi‐Method Comparative Bioinformatics Pipeline to Detect and Study circRNAs from RNA‐seq Data[J]. Non-Coding RNA, 2017, 3(1): 8.

３、Cox J W, Ballweg R A, Taft D H, et al. A fast and robust protocol for metataxonomic analysis using RNAseq data[J]. Microbiome, 2017, 5(1):7.

４、Wilks C, Gaddipati P, Nellore A, et al. Snaptron: querying and visualizing splicing across tens of thousands of RNA-seq samples[J]. bioRxiv, 2017: 097881.

５、Rodríguezmartín B, Palumbo E, Marcosola S, et al. ChimPipe: accurate detection of fusion genes and transcription-induced chimeras from RNA-seq data[J]. Bmc Genomics, 2017, 18(1):7.

６、Hastreiter M, Jeske T, Hoser J, et al. KNIME4NGS: a comprehensive toolbox for Next[J]. 2017.

７、Paciello G, Ficarra E. FuGePrior: A novel gene fusion prioritization algorithm based on accurate fusion structure analysis in cancer RNA-seq samples[J]. 2017, 18.

８、Li Y, Andrade J. DEApp: an interactive web interface for differential expression analysis of next generation sequence data[J]. Source Code for Biology and Medicine, 2017, 12(1): 2.

９、Van B M, Coppens F. Exploring Plant Co-Expression and Gene-Gene Interactions with CORNET 3.0[J]. Methods in Molecular Biology, 2017, 1533:201.

10、Evans C, Hardin J, Stoebel D. Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions[J]. 2016.