Category: Rna seq analysis pipeline cufflinks

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Rna seq analysis pipeline cufflinks

Work fast with our official CLI. Learn more. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. This results in a Mappings table containing all mapped reads and a table containing per-gene expression level represented in FPKM values Fragments Per Kilobase of transcript per Million mapped reads.

Full output of the cufflinks program is also output as a tar file which also includes expression on the per isoform level. This pipeline does not support novel gene or isoform discovery.

Reads will only be mapped to transcripts found in the input Genes object. This corresponds to running Tophat with the "-G" and "--transcriptome-index" options.

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While these options will always be passed to Tophat, further options modifying both Tophat and Cufflinks steps are accepted by the app. The input is an array of gtables of type Reads, which contain the RNA-seq reads. The pipeline support both paired and unpaired reads but all inputs must be of the same type either all paired or all unpaired. The reference genome for mapping is provided in the form of a ContigSet object.

The genome must be compatible with the gene models supplied. Alternatively, a Bowtiev2 indexed copy of the reference genome can be provided. If not supplied, an index will be generated, a process that may take up to several hours. The indexed genome will then be included as part of the app output, and can be provided in later runs of the app.

Gene models are provided as a Genes object describing the transcripts to map reads to. This parameter is a string containing all additional options to be passed to Tophat during execution. A guide to Tophat options can be found here. As mentioned above the "-G" and "--transcriptome-index" options will always be passed to Tophat. The string input here will be passed directly to the Tophat program and therefore must be formatted as it would be on the command line. Contradictory or invalid parameters will not be caught and will cause the app to fail.

This optional parameter is a string containing additional options to be passed to Cufflinks during execution. Cufflinks takes the mappings generated by Tophat and calculates the level of expression of each gene and transcript. A guide to Cufflinks options can be found here The pipeline uses the "-G", "-p", and "-o" options to input the gene model, use all processors, and capture the output.

Do not include these as further parameters. No additional options are set by default. Skip to content. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Git stats 42 commits.Pipeline comparisons for gene expression data are highly valuable for applied real data analyses, as they enable the selection of suitable analysis strategies for the dataset at hand.

Such pipelines for RNA-Seq data should include mapping of reads, counting and differential gene expression analysis or preprocessing, normalization and differential gene expression in case of microarray analysis, in order to give a global insight into pipeline performances. For these comparisons we generated two matched microarray and RNA-Seq datasets: Burkitt Lymphoma cell line data and rectal cancer patient data. The overall mapping rate of STAR was For both datasets we identified very low numbers of differentially expressed genes using the microarray platform.

For RNA-Seq we checked the agreement of differentially expressed genes identified in the different pipelines and of GO-term enrichment results.

rna seq analysis pipeline cufflinks

This is an open access article distributed under the terms of the Creative Commons Attribution Licensewhich permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Competing interests: The authors have declared that no competing interests exist. Transcriptomics as an area in the research field of functional genomics has always been a key player for identifying interactions and regulations of gene expression.

Over the last two decades it was common practice to use microarrays for any investigation in transcriptomics. These technologies are gradually replacing microarrays, when analyzing and identifying complex mechanism in gene expression. Decreasing running costs, higher dynamic range of expression and higher accuracy in low abundance measurements [ 2 ] are the main factors for this fast development of NGS and increasing use of RNA-Seq over microarray. Another advantage is the currently highly discussed variant calling [ 4 ] [ 5 ] [ 6 ] based on RNA-Seq data, which makes this technology even more attractive.

The developments of new technologies, like Pacific Bioscience or Nanopore [ 7 ], can further contribute in the field of RNA-Seq and transcriptomics in form of more detailed annotation databases in the future.

A typical application for RNA-Seq is the differential gene expression analysis. First, millions of short reads are produced, which are mapped to a reference genome.

Subsequently, the amount of reads mapping to a genomic feature of interest for example a gene, transcript or exon is measured as the abundance of these features [ 8 ]. The abundance per feature is used as an input for differential expression analysis. Still, microarrays are widely used because of their lower costs compared to the RNA-Seq technology. Moreover, there are large and well maintained repositories, such as ArrayExpress [ 9 ] and Gene Expression Omnibus GEO [ 10 ], that have collected the microarray data over long time periods.

While the preprocessing and analysis steps of microarray data are mostly standardized, the establishment of RNA-Seq data analysis methodology and standards is still ongoing in the field of transcriptomics. A lot of efforts have been performed into method comparison studies to change this [ 11 ] [ 12 ] [ 13 ] [ 14 ]. The quality evaluation of different RNA-Seq pre- processing methods are one important step to establish a quality standard.

We aim to investigate commonly used RNA-Seq pipelines on multiple levels alignment, counting and cross-compare the results with the microarray counterpart on the level of gene expression and gene ontology enrichment. For these evaluations we generated two matched microarray and RNA-Seq datasets: rectal cancer RC patient data good versus bad prognosis patients and Burkitt Lymphoma BL2 cell line data control versus stimulated cells.

Fragmentation and hybridization on Human ST1. Patients were chosen based on the follow-up time and development of a distant metastasis. First a balanced sample size of five versus five patients with and without a metastatic event was intended. A later development of metastasis of one of the good prognosis patients changed the sample size to 6 versus 4 patients. Slides were washed and scanned using an Agilent GBA scanner.

All preprocessing and statistical analyses of microarray data were performed using R statistical computing environment [ 19 ].

Bioinformatics for RNAseq

Adittional quality control metrics for BL2 can be found in the supplements S1 File. Both datasets were log2 transformed and quantile normalized. In case of several probes corresponding to the same Ensembl gene identifier, the probe with median expression intensities was chosen to represent the gene level expression.

Differential expression analysis was performed by fitting linear models using empirical Bayes method as implemented in the limma r-package [ 21 ] and p-values were adjusted for multiple testing using Benjamini-Hochberg BH method [ 22 ]. Beside an agglomeration of nucleotides with slightly lower quality at the starting positions than in the middle of reads, no major quality issues were observed S1 TableS1a and S1b Fig.But maybe it can be a starting point.

No approach is perfect.

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One job to rule them all. But, not having time to make my pipeline absolutely perfect, I found that some fatal problem or another would always arise but the job-to-rule-them-all would have already moved on and wasted thousands of CPU hours on subsequent steps before I noticed. So my first step is to get a file listing the full paths of all FASTQs along with metadata parsed from the filenames.

The Cufflinks RNA-Seq workflow

And then start parsing out the metadata. Like so:. Next I re-split on period, slash and underscore -F"[. And at some point I realized I also need the base filename as opposed to full path of the FASTQs, for copying them into temporary directories.

So I added yet two more columns:.

rna seq analysis pipeline cufflinks

By the way, the code above represents a best case where the metadata are at least formatted identically in all FASTQ filenames. By the way, another nice trick you can do at this stage is to create a dummy file with a tiny subset of the data from one of your samples, that will run very quickly and you can use to test out each step in your pipeline before going all-in and submitting hundreds of jobs. For instance. This will save you a lot of failed jobs when you have some silly typo in your code.

Make sure you have the right reference genome for alignment and transcript feature file for calling expression levels and that they match. The idea here is to first put all the commands you plan to run into a. Once my FastQC jobs finish, I usually visually inspect one or two the HTML-formatted summaries and then aggregate all the data together to do summary statistics.

And then you can read them into R or any SQL database and play with them using some of the queries in the original post. It makes me shudder just to look at. What makes this so unreadable is the multiple layers of escaping of quotes. Hence why I write it first to submitalignmentjobs.

The ugliness and the potential for mixups when doing so much escaping is one of the reasons I say that this pipeline surely is not the best way of getting this job done.

This avoids all the escaping but has the tradeoff of creating a lot of clutterfiles you have to clean up. Now, once my alignments have finished, I want to check that the BAMs all turned out valid. Curiously, I have found that sometimes a job will appear to finish and will create a valid BAM which samtools can read without errors, yet which appears to be truncated based on its size.

There are also usually a few BAMs that are larger than they should be dots towards the right. Note that when I first aligned the samples I just submitted them as jobs. But now that I have in the example above just one sample that needs to be re-aligned, I can benefit from splitting it up to run in an hour or so instead of a few days.

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Then I check how many files this created, loop through them all and submit a separate Gsnap job for each. Then I samtools merge the bams back together. Or alternately if you had more than one sample fail, you can grep -f the badbam list against the fastq. You can cat fastq. Again, I copy everything to a temporary directory and then back again.

See my post on counts vs. It seemed that this process was missing the gene expression analogue of joint calling of genetic variants, as for instance in exome sequencing. Which took under a half hour.

In my particular project, we have a couple of different research questions, some of which revolve around gene expression hence Cufflinks or some other tool, above and others of which will require some custom scripting to look at coverage around particular splice sites and polyadenylation sites. Therefore among other things I want to calculate read depth for a region of interest, which I do here using BEDtools. In this case, I want mouse huntingtin called Hdh or Htt depending on who you ask as well as the knock-in construct that I added to my reference genome.Environment requirements:.

The following configure wizard appears:. Here you need to choose analysis type and short reads type and click Setup. There are two short reads type: single-end and paired-end reads. For both of them there are three analysis type:. For Full Tuxedo Pipeline analysis type and single-end reads type the following workflow appears:. Use the workflow wizard to guide you through the parameters setup process.

Click Show wizard button on the Workflow Designer toolbar to open it:. All of these workflows have the similar wizards. Many datasets with different reads can be added. Click the Next button. The next page appears:. Here you need to divide the input datasets into samples for running Cuffdiff. There are must be at least 2 samples. It is not neccessary to have the same number of datasets replicates for each sample. The samples names will be used by Cuffdiff as labels, which will be included in various output files produced by Cuffdiff.

Here you can configure TopHat settings. The following parameters are available:. Standard deviation for the distribution on inner distances between mate pairs. Only look for reads across junctions indicated in the supplied GFF or junctions file. This parameter is ignored if Raw junctions or Known transcript file is not set. Instructs TopHat to allow up to this many alignments to the reference for a given read, and suppresses all alignments for reads with more than this many alignments.

Each read is cut up into segments, each at least this long. These segments are mapped independently. Only align the reads to the transcriptome and report only those mappings as genomic mappings. When mapping reads on the transcriptome, some repetitive or low complexity reads that would be discarded in the context of the genome may appear to align to the transcript sequences and thus may end up reported as mapped to those genes only.

This option directs TopHat to first align the reads to the whole genome in order to determine and exclude such multi-mapped reads according to the value of the Max multihits option. The anchor length. TopHat will report junctions spanned by reads with at least this many bases on each side of the junction. Note that individual spliced alignments may span a junction with fewer than this many bases on one side.

However, every junction involved in spliced alignments is supported by at least one read with this many bases on each side.There are two short reads types of workflow: single-end and paired-end reads. For both of them there are three analysis types:. All of these workflows have the similar wizards. Many datasets with different reads can be added. There are must be at least 2 samples. It is not necessary to have the same number of datasets replicates for each sample.

The samples names will be used by Cuffdiff as labels, which will be included in various output files produced by Cuffdiff. Standard deviation for the distribution on inner distances between mate pairs. Only look for reads across junctions indicated in the supplied GFF or junctions file. This parameter is ignored if Raw junctions or Known transcript file is not set. Instructs TopHat to allow up to this many alignments to the reference for a given read, and suppresses all alignments for reads with more than this many alignments.

Each read is cut up into segments, each at least this long. These segments are mapped independently. Only align the reads to the transcriptome and report only those mappings as genomic mappings. When mapping reads on the transcriptome, some repetitive or low complexity reads that would be discarded in the context of the genome may appear to align to the transcript sequences and thus may end up reported as mapped to those genes only.

This option directs TopHat to first align the reads to the whole genome in order to determine and exclude such multi-mapped reads according to the value of the Max multihits option. The anchor length. TopHat will report junctions spanned by reads with at least this many bases on each side of the junction. Note that individual spliced alignments may span a junction with fewer than this many bases on one side. However, every junction involved in spliced alignments is supported by at least one read with this many bases on each side.

The maximum number of mismatches that may appear in the anchor region of a spliced alignment. Final read alignments having more than these many mismatches are discarded. Read segments are mapped independently, allowing up to this many mismatches in each segment alignment. As of the Illumina GA pipeline version 1. TopHat uses -v in Bowtie for initial read mapping the defaultbut with this option, -n is used instead. Read segments are always mapped using -v option. The path to the SAMtools tool.

Tells Cufflinks to use the supplied reference annotation to estimate isoform expression. Cufflinks will not assemble novel transcripts and the program will ignore alignments not structurally compatible with any reference transcript. Reference transcripts will be tiled with faux-reads to provide additional information in an assembly. The output will include all reference transcripts as well as any novel genes and isoforms that are assembled. Ignore all reads that could have come from transcripts in this file.

It is recommended to include any annotated rRNA, mitochondrial transcripts other abundant transcripts you wish to ignore in your analysis in this file. Due to variable efficiency of mRNA enrichment methods and rRNA depletion kits, masking these transcripts often improves the overall robustness of transcript abundance estimates.

rna seq analysis pipeline cufflinks

Tells Cufflinks to do an initial estimation procedure to more accurately weight reads mapping to multiple locations in the genome. After calculating isoform abundance for a gene, Cufflinks filters out transcripts that it believes are very low abundance, because isoforms expressed at extremely low levels often cannot reliably be assembled, and may even be artifacts of incompletely spliced precursors of processed transcripts.

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This parameter is also used to filter out introns that have far fewer spliced alignments supporting them.

Providing Cufflinks with a multifasta file via this option instructs it to run the bias detection and correction algorithm which can significantly improve accuracy of transcript abundance estimates.

Some RNA-Seq protocols produce a significant amount of reads that originate from incompletely spliced transcripts, and these reads can confound the assembly of fully spliced mRNAs.Cufflinks [1] is an integrated transcriptome analysis tool from transcript assembles, quantification, and comparison between different conditions.

There are three major steps involved in Cufflinks, including Cuffilnks, Cuffmerge, and Cuffdiff. The first step of Cufflinks is to assemble all possible transcripts with the results of alignment from TopHat. To accurately estimate the abundance of each transcript, any possible isoforms are considered in this step.

Then, Cuffmerge merges the previous assembles of individual condition. With the step of merging assembles, the whole genes can be recovered, and the de novo transcripts can also be integrated into the complete gene model. At last, the differential analysis is conducted by Cuffdiff. Cufflinks accept the standard format of short reads alignment.

SAM, or a binary form. It does recommend using the results from TopHat. However, it should be noticed the alignment file should be with a special tag, XS, and be sorted by reference position. More details are described on the website of Cufflinks. How does Cufflinks work? Differential files: These files include the expression comparison at several levels, including genes, transcripts isoformstranscription start sites TSScoding site CDSsplicing and promoter. The values of fold change, P-value, and multiple-test adjusted q-value are provided.

Info files: Two files introducing command line and information of Cufflinks are included. One is simple version and the other contains more detailed information. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation.

Nature biotechnology 28,doi Trapnell, C.An introduction to data integration and statistical methods used in contemporary Systems Biology, Bioinformatics and Systems Pharmacology research.

An mRNA-seq pipeline using Gsnap, samtools, Cufflinks and BEDtools

The course covers methods to process raw data from genome-wide mRNA expression studies microarrays and RNA-seq including data normalization, differential expression, clustering, enrichment analysis and network construction.

The course contains practical tutorials for using tools and setting up pipelines, but it also covers the mathematics behind the methods applied within the tools.

The course is mostly appropriate for beginning graduate students and advanced undergraduates majoring in fields such as biology, math, physics, chemistry, computer science, biomedical and electrical engineering.

The course should be useful for researchers who encounter large datasets in their own research. The ultimate aim of the course is to enable participants to utilize the methods presented in this course for analyzing their own data for their own projects.

For those participants that do not work in the field, the course introduces the current research challenges faced in the field of computational systems biology. Excellent course to get deep into the data analysis of system biology experimentation. A set of lectures in the 'Deep Sequencing Data Processing and Analysis' module will cover the basic steps and popular pipelines to analyze RNA-seq and ChIP-seq data going from the raw data to gene lists to figures.

Note that since these lectures were developed and recorded during the Fall ofit is possible that there are better tools that should be used now since the field is rapidly advancing. Network Analysis in Systems Biology. Course 3 of 6 in the Systems Biology and Biotechnology Specialization.

Enroll for Free. This Course Video Transcript. Taught By. Try the Course for Free. Explore our Catalog Join for free and get personalized recommendations, updates and offers. Get Started. Learn Anywhere. All rights reserved.


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