Rna seq pipeline
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The document discusses RNA-Seq data analysis. Some key points: - RNA-Seq involves sequencing steady-state RNA in a sample without prior knowledge of the organism. It can uncover novel transcripts and isoforms. - Making sense of the large and complex RNA-Seq data depends on the scientific question, such as finding transcribed SNPs for allele-specific expression or novel transcripts in cancer samples. - Common applications of RNA-Seq include abundance estimation, alternative splicing detection, RNA editing discovery, and finding novel transcripts and isoforms. - Analysis steps include mapping reads to a reference genome/transcriptome, generating mapping statistics and quality metrics, differential expression analysis, clustering, and pathway analysis using tools like
![Summarization : Parameters
Transcripts: The number of transcripts based on the mRNA
annotations on the reference. Note that this is not based on the
sequencing data - only on the annotations already on the reference
sequence(s).
Exon length: The total length of all exons (not all transcripts).
Unique gene reads : This is the number of reads that match uniquely to
the gene.
Total gene reads: This is all the reads that are mapped to this gene --both reads that map uniquely to the gene and reads that matched to
more positions in the reference (but fewer than the ’Maximum
number of hits for a read’ parameter) which were assigned to this
gene.
RPKM: Reads Per Kilobase of exon model per Million mapped reads is
the expression value measured in RPKM [Mortazavi et al., 2008]:
RPKM = total exon reads/ mapped reads(millions)exon length (KB) .](https://image.slidesharecdn.com/rna-seqpipeline-140123002951-phpapp02/85/Rna-seq-pipeline-15-320.jpg)
Rna seq pipeline
- 1. RNA-Seq Data Analysis National Bureau of Animal Genetic Resources Karnal
- 2. Transcriptome Sequencing Sequencing steady state RNA in a sample is known as RNA-Seq. It is free of limitations such as prior knowledge about the organism is not required. RNA-Seq is useful to unravel inaccessible complexities of transcriptomics such as finding novel transcripts and isoforms. Data set produced is large and complex; interpretation is not straight forward.
- 3. Making sense of RNA-Seq data……. Depends upon the scientific question of interest. For example allele specific expression requires accurate determination of the transcribed SNPs. Finding novel transcripts will help in finding fusion gene events and aberrations in cancer samples.
- 4. Applications of RNA-Seq Abundance estimation 2. Alternative splicing 3. RNA editing 4. Finding novel transcripts 5. Finding isoforms And many more….. 1.
- 5. From RNA-seq reads to differential expression results: Oshlack et al. Genome Biology 2010, 11:220
- 6. Mapping Reads to Reference: CLC bio Workbench The RNA-Seq analysis is done in several steps: First, all genes are extracted from the reference genome (using annotations of type gene). Other annotations on the gene sequences are preserved (e.g. CDS information about coding sequences etc). Next, all annotated transcripts (using annotations of type mRNA) are extracted. If there are several annotated splice variants, they are all extracted. Note that the mRNA annotation type is used for extracting the exon-exon boundaries.
- 8. The mapping parameters Maximum number of mismatches : short reads (shorter than 56 nucleotides, except for color space data which are always treated as long reads). This is the maximum number of mismatches to be allowed. Maximum value is 3, except for color space where it is 2. Minimum length fraction : the default is 0.9 which means that at least 90 % of the bases need to align to the reference. Minimum similarity fraction : the default setting at 0.8 and the default setting for the length fraction, it means that 90 % of the read should align with 80 % similarity in order to include the read. Maximum number of hits for a read : a read that matches to more distinct places in the references than the ’Maximum number of hits for a read’ specified will not be mapped Strand-specific alignment : Mapping reads to specific strand
- 10. Summarization
- 11. Summarization
- 12. Summarization
- 13. Summarization : Mapping Statistics
- 14. Summarization : Detailed Mapping Statistics
- 15. Summarization : Parameters Transcripts: The number of transcripts based on the mRNA annotations on the reference. Note that this is not based on the sequencing data - only on the annotations already on the reference sequence(s). Exon length: The total length of all exons (not all transcripts). Unique gene reads : This is the number of reads that match uniquely to the gene. Total gene reads: This is all the reads that are mapped to this gene --both reads that map uniquely to the gene and reads that matched to more positions in the reference (but fewer than the ’Maximum number of hits for a read’ parameter) which were assigned to this gene. RPKM: Reads Per Kilobase of exon model per Million mapped reads is the expression value measured in RPKM [Mortazavi et al., 2008]: RPKM = total exon reads/ mapped reads(millions)exon length (KB) .
- 18. Basic Statistics Summary The Basic Statistics module generates some simple composition statistics for the file analysed. Filename: The original filename of the file which was analysed. File type: Says whether the file appeared to contain actual base calls or colorspace data which had to be converted to base calls. Total Sequences: A count of the total number of sequences processed. There are two values reported, actual and estimated. Sequence Length: Provides the length of the shortest and longest sequence in the set. If all sequences are the same length only one value is reported. %GC: The overall %GC of all bases in all sequences Warning Basic Statistics never raises a warning.
- 19. This view shows an overview of the range of quality values across all bases at each position in the FastQ file. For each position a BoxWhisker type plot is drawn. The elements of the plot are as follows: The central red line is the median value The yellow box represents quartilerange (25-75%) The upper and lower whiskers represent the10% and 90% points the inter- The blue line represents the mean quality. The y-axis on the graph shows the quality scores. The higher the score the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read. It should be mentioned that there are number of different ways to encode a quality score in a FastQ file. FastQC attempts to automatically determine which encoding method was used, the title of the graph will describe the encoding FastQC thinks your file used.
- 20. The per sequence quality score report allows you to see if a subset of your sequences have Universally low quality values. It is often the case that a subset of sequences will have universally poor quality, often because they are poorly imaged (on the edge of the field of view etc), however these should represent only a small percentage of the total sequences. If a significant proportion of the sequences in a run have overall low quality then this could indicate some kind of systematic problem - possibly with just part of the run (for example one end of a flowcell).
- 21. Normalization
- 23. Clustering
- 24. Comparison of Expression Profile
- 25. Expression Profile of Specific Pathways
- 26. Systems Biology : Gostat Analysis Best GOs Genes (Max: 100) GO:0003735 Mitochondria mrpl42 mrpl41 ndufa13 ndufb5 timm13 etfb ndufa3 atp5d atp5j2 ndufb7 mrpl14 ndufa5 ndufa11 mrpl34 GO:0005840 Ribosome rps2 mrpl42 rps18 rps17 mrpl41 rps23 mrps18c rplp2 mrpl14 rpl9 rps29 mrpl34 Count 150 12 Total 18253 156 12 163 P-Value 4.78E-06 4.78E-06