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How to analyze RNA Seq data with Python? 

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Our software provides tools to better understand error profiles in RNA-Seq data and improve inference from this new technology.
Our pipeline facilitates the comprehensive and efficient analysis of private and public RNA-seq data.
The results on simulation and real RNA-Seq data demonstrate that our method is effective and suitable for analyzing these data.
Open accessJournal ArticleDOI
Matthias Zytnicki, Hadi Quesneville 
06 Oct 2011-PLOS ONE
37 Citations
With this tool, biologists can easily perform most of the analyses on their computer for their RNA-Seq data, from the mapped data to the discovery of important loci.
It is a useful method designed for comparing two groups of RNA-seq samples.
Applying the protocol described here and using the provided data sets represent a useful first step toward building RNA-Seq data analysis expertise in a research group.
We have evaluated different algorithms available for the analysis of RNA-seq and proposed a novel approach--NOISeq--that differs from existing methods in that it is data-adaptive and nonparametric.
The selection of bioinformatic software tools for RNA-seq data analysis can maximize quality parameters on de novo assemblies and availability of reads in downstream analysis.
Open accessPosted Content
162 Citations
We present a novel approach to RNA-Seq quantification that is near optimal in speed and accuracy.
This highlights an important layer of variation that remains undetected by current protocols for RNA-seq analysis.

Related Questions

What are the potential applications of using Python to analyze biological sequences in research and industry?5 answersPython offers diverse applications in analyzing biological sequences for research and industry. In research, Python, with libraries like NumPy, Pandas, and Scikit-learn, facilitates predicting type 2 diabetes from insulin DNA sequences, aiding in understanding gene variants and risk factors. Additionally, tools like BioNumPyenable efficient handling of biological datasets, bridging the gap between simple scripts and large-scale data processing. Python tools like ortho_seqsprovide a method to quantify sequence-phenotype interactions, enhancing the understanding of genetic impacts. In industry, Python can optimize job sequencing problems, reducing time and errors in tasks like production planning. Overall, Python's versatility in data handling, machine learning, and efficient processing makes it invaluable for analyzing biological sequences in both research and industry settings.
What are techniques for the analysis of multiplexed RNA-ISH data?5 answersLigation in situ Hybridization (LISH) is a technique that utilizes T4 RNA Ligase 2 to join adjacent chimeric RNA-DNA probe pairs hybridized in situ on fixed RNA target sequences. This technique has several advantages including >100-plex capability, compatibility with common histochemical stains, and suitability for analysis of decade-old materials and small tissue fragments. The proximity ligation assay for RNA (PLAYR) is another technique that relies on in situ and proximal ligation to measure specific RNAs by flow or mass cytometry. It allows simultaneous highly multiplexed profiling of RNA and protein expression at single-cell resolution. Multiplex nucleic acid amplification reactions can be used to amplify and characterize a plurality of different target sequences-of-interest in a single reaction mixture, enabling simultaneous analysis of multiple RNA targets. Multiplexed error-robust fluorescence in situ hybridization (MERFISH) is a technique that allows simultaneous imaging of numerous RNA species in their native cellular environment. It can be combined with branched DNA (bDNA) amplification to increase signal brightness and detection efficiency.
What statistical tools are available for analyzing gene expression data?4 answersVarious statistical tools are available for analyzing gene expression data. These tools are used for comparative analysis, classification of sample observations, discovery of feature genes, and characterization of intra-subject variation. They also include methods for imputation of missing values, data normalization, feature selection, feature extraction, and classification and class discovery. These tools can be applied to different types of gene expression data, such as microarray technology, RNA sequencing, and quantitative proteomics datasets. The use of statistical power analysis is also important in transcriptomic studies to ensure biologically meaningful findings. Power analysis tools exist for bulk RNA-seq and single-cell RNA-seq experiments, with recommendations provided. However, there are currently no power analysis tools available for high-throughput spatial transcriptomics, but factors influencing power analysis are investigated.
How to analyze single cell transcriptomic data?5 answersTo analyze single cell transcriptomic data, several steps can be followed. First, data preprocessing is necessary to increase the signal-to-noise ratio and remove variability. Next, feature selection methods can be applied to identify genes that vary between cell types and capture both discrete and continuous transcriptional patterns. Clustering algorithms can then be used to group similar cell types together. Additionally, gene-gene correlations can be identified to uncover potential relationships and biological processes. Visualization tools can aid in exploring the data and identifying patterns. Finally, differential gene expression analysis can be performed to identify genes that differ between clusters. These steps provide a comprehensive approach to analyzing single cell transcriptomic data, allowing for the identification of cell types, markers, and continuous variation within and between cell types.
How to analyze RNA-seq gene expression data generated from different studies in GDC database?5 answersRNA-seq gene expression data generated from different studies in the GDC database can be analyzed using various methods. One approach is to utilize deep learning models, which have been successful in several areas but pose challenges in biomedical research due to limited sample sizes. Another method involves comparing gene expression across studies using standardized mapping, normalization, and clustering methods, which can provide insights into gene expression evolution and enable meta-analyses. Additionally, the sleuth method, implemented in an interactive shiny app, utilizes bootstrapping and response error linear modeling to analyze RNA-seq data. It is also important to consider the nature of RNA-seq data, including the choice between working with count data or transformed data, as well as the appropriate distribution models for normalization and comparison.
How do you analyze RNA Seq data in Python?10 answers

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