RNA-seq for Transcriptome profiling and discovery of novel transcripts and alternatively spliced variants using HPC Presented by: Al Ritacco, Shailender.

RNA-seq for Transcriptome profiling and discovery of novel transcripts and alternatively spliced variants

using HPC

Presented by:Al Ritacco, Shailender Nagpal

Research ComputingUMASS Medical School

09/17/2012Information Services,

Agenda

SESSION 1– What is RNA-seq?– Workflow for RNA-seq analysis– Tools required

SESSION 2– Download and perform QC on sample dataset– Mapping and alignment– Transcript expression and other “discoveries”

00/00/2010Information Services,2

Agenda




What is “Next Generation Sequencing”?

• Set of new high throughput technologies– allow millions of short DNA sequences from a biological

sample to be “read” or sequenced in a rapid manner– Computational power is then used to assemble or align

the “reads” to a reference genome, allowing biologists to make comparisons and interpret various biological phenomena

• Due to high depth of coverage (30-100x), accurate sequencing is obtained much faster and cheaper compared to traditional Sanger/Shotgun sequencing

RNA-Seq experiment

• Reverse transcription of mRNAs yield double stranded cDNAs, which are sliced to selected fragment length

What is RNA-Seq?

• RNA-seq is a Next Generation Sequencing (NGS) technology for sequencing total mRNA (“expressed”) in biological samples of interest such as tissues, tumors and cell lines– Provides deep coverage and base level resolution– Abundance of known transcripts– Novel transcripts– Alternative splicing events– Post-transcriptional mutations– Gene fusions

RNA-seq reads: Issue 1

• Small, single-end reads are hard to align to a reference genome - multiple possible mapping sites. Longer reads can overcome this limitation

• Paired ends allow for longer fragments to be sequenced, with a small read from each end of the fragment. Distance between ends validates mapping

ACTTAAGGCTGACTAGC TCGTACCGATATGCTG

RNA-seq reads: Issue 2

• Reads from one exon are easily mapped• Reads that span 2 exons are handled by

special software like TopHat


DNA assembly versus alignment

• DNA assembly is the computational task of putting together pieces of the genome in the original order– Overlapping short sequences are extended to form

islands, that are subsequently extended and merged– Used for denovo sequencing of a new genome

• DNA alignment is the computational task of mapping short reads to a known, sequenced genome– Reads and searched for in the full genome sequence, then

aligned with a local alignment algorithm

Agenda




RNA-seq workflow summary

• Sample preparation and submission for experiment. Obtain “read” file(s) as FASTQ

• Perform QC• Perform read alignment and mapping to reference

genome• Determine expression, novel transcripts and

alternative splicing

Step 1: Working with FASTQ Reads

• After a sample has been processed by an NGS platform, DNA sequence “reads” are provided to the user in FASTQ format

• FASTQ is a text-based format for storing both a DNA sequence and its corresponding quality scores – sequence letter and quality score are encoded with a

single ASCII character for brevity– originally developed at the Wellcome Trust Sanger

Institute to bundle a FASTA sequence and its quality data– recently become the de facto standard for storing the

output of high throughput sequencing instruments

Type of reads

• Single end reads– refer to the sequence determined as DNA bases are

added to single stranded DNA and detected, usually from one end only

• Paired end reads– refer to the two ends of the same DNA molecule– After sequencing one end, you can turn it around and

sequence the other end. – Long segment of DNA in between the two ends (usually

200-500 bp), who’s sequence is unknown– Once the two paired end reads are mapped, the

intermediate sequence can be inferred from reference sequence

Quality Score

• A quality value Q is an integer mapping of p (i.e., the probability that the corresponding base call is incorrect)

• Two different equations have been in use. The first is the standard Sanger variant to assess reliability of a base call, otherwise known as Phred quality score:– The Solexa pipeline earlier used a different mapping,

encoding the odds p/(1-p) instead of the probability p:– Although both mappings are asymptotically identical at

higher quality values, they differ at lower quality levels (i.e., approximately p > 0.05, or equivalently, Q < 13)

Quality Score Encoding

• Various platform produce versions of FASTQ format, which mainly differ in the Quality score representation

• Converters from various tools can convert Solexa to Sanger FASTQ format

Step 2: Quality Control

• FASTQ, PRINSEQ or other custom tools can be used to perform QC on FASTQ files

• Good tutorial for FASTQC on Youtube:https://www.youtube.com/watch?v=bz93ReOv87Y


https://www.youtube.com/watch?v=bz93ReOv87Y

https://www.youtube.com/watch?v=bz93ReOv87Y

Step 3: Alignment/Mapping to Reference Genome

• Many tools exist that will map the reads to the reference genome and align them, generating a quality score per base of alignment– BLAST-like algorithm to search the read in the genome– Local alignment to determine the accuracy of the

alignment

• Many tools (SAMtools, MAQ, TopHat, CASSAVA, etc) use the BWA, Bowtie and Eland algorithms.Result of alignment is the SAM file

Alignment algorithms

• A category of aligners that hash the reads and scan the genome for matches– Eland, RMAP, MAQ, ZOOM, SeqMap, CloudBurst,

SHRiMP

• Disadvantages– For few reads, whole genome must be scanned– Memory footprint is variable

Alignment algorithms (…contd)

• Another category of aligners hash the reference genome– SOAPv1, PASS, MOM, ProbeMatch, NovoAlign, ReSeq,

Mosaik, Bfast

• Easily parallelized with multi-threading, but they usually require large memory to build an index for the human genome

• Disadvantage: iterative strategy frequently introduced by these software may make their speed sensitive to the sequencing error rate

Alignment algorithms (…contd)

• A third category which does alignment by merge-sorting the reference subsequences and read sequences– Slider

• Burrows–Wheeler Transform (BWT) for string matching has been incorporated into a new generation of alignment tools– SOAPv2, Bowtie and BWA– These are memory efficient and suit well to single

and paired-end read alignments

Step 4: Transcript expression

• “Cufflinks”, “myRNA” and other software can estimate the expression level/ abundance of the transcripts

• Other discoveries possible – novel transcripts, fusions, etc.

Agenda




FASTQ manipulation tools

• Galaxy FASTQ toolshttps://bitbucket.org/galaxy/galaxy-central/src/tip/tools/fastq

• FASTXtoolkithttp://hannonlab.cshl.edu/fastx_toolkit

QC software

• FASTqchttp://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/

• PRINSEQ http://prinseq.sourceforge.net


Alignment software

• Single and paired-end alignments can be done using the BWA algorithm in the following software– MAQ (http://maq.sourceforget.net)– BWA– Bowtie– TopHat (http://tophat.cbcb.umd.edu)

• Alignments are produced generally in the widely accepted SAM format

http://maq.sourceforget.net/

http://tophat.cbcb.umd.edu/

Step 4: Transcript expression

• Cufflinks• myRNA

Agenda




Computing Hardware Requirements

• Two types of computing hardware are ideally suited for NGS data analysis– High-end workstation, for example: 64-bit linux, 3.6 GHz

quad-core processor, 32 GB RAM, 7200 rpm hard disk– High Performance Computing cluster (HPC) where total

execution time can be sped up – for example, split reads into small files and align them in parallel on dozens of nodes

• For this workshop, we will use HPC

RNA-seq datasets

• NCBI’s Short Read Archive is a good source of datasets. Data can be download from: http://www.ncbi.nlm.nih.gov/sra– Look for HapMap, 1000 genomes, cancer samples – all types of

experiment types and platforms– Combine keywords in search: “Illumina” and “Paired” and

“RNA-seq” and “HapMap”

http://www.ncbi.nlm.nih.gov/sra

Obtain data from SRA

• Dataset to be used:– Prostate cancer

http://www.ncbi.nlm.nih.gov/sra/SRX022065 – Tumor-matched Normal

http://www.ncbi.nlm.nih.gov/sra/SRX022083

• These are two paired-end Solexa datasets with reads split by those belong to the forward or end of a sequence fragment





Setting up the data for analysis

• Create directory for this dataset

cd /home/username

mkdir rna-seqcd rna-seq


Load RNA-seq tools

• Load TopHat, Bowtie, Cufflinks and Samtools

module load tophat-1.2.0module load cufflinks-2.0.2module load bowtie-0.12.7module load samtools-0.1.18module load sratoolkit.2.1.9

Download Genome Annotation (GTF) file

• The genome annotation file contains exon/intron co-ordinates of reference genome

Download the dataset

• Download the Paired-end reads for the following library

wget ftp://ftp-trace.ncbi.nlm.nih.gov/sra/sra-instant/reads/ByRun/sra/SRR/SRR057/SRR057652/SRR057652.sra

wget ftp://ftp-trace.ncbi.nlm.nih.gov/sra/sra-instant/reads/ByRun/sra/SRR/SRR057/SRR057634/SRR057634.sra

SRA tools to extract FASTQ files

• Use "sratools" to convert SRA format to FASTQfastq-dump -A SRR057634 --split-3

SRR057634.srafastq-dump -A SRR057652 --split-3

SRR057652.sra


Perform QC using FASTQC

• Load FASTQC modulemodule load fastqc-1.0

• Perform QCfastqc –t 8 SRR057634_1.fastqfastqc –t 8 SRR057634_2.fastq

• This creates 2 directories with output in HTML format for visual inspection in a browser, key statistics and tests are in text file

• The zip files generated can be deleted

Agenda




TopHat-Cufflinks


Alignment and Mapping

• For RNA-seq, the reads will either map fully to exons or partially to exon-intron junctions, resulting in rejected reads for the latter caseo Should not use Bowtie or BWA directly for mapping

against a reference genomeo one of the goals is to identify novel transcripts, so we

should not use transcriptome as reference

• TopHat is ideally suited for the job, uses Bowtie for read alignmento needs the reference genome and it's annotations as an

input. Annotations are optional

Running TopHat for read alignment

• To run TopHat, execute the following command:tophat --num-threads 8 \--solexa-quals --max-multihits 10 \--coverage-search --microexon-search \--mate-inner-dist 150 \-o tophat-tumor-out \--keep-tmp \–G /usr/public_data/ucsc/genomes/hg19/hg19.gtf \ hg19 \SRR057634_1.fastq \SRR057634_2.fastq

Working with TopHat output

• This produces the “tophat-tumor-out” folder with the following files:

accepted_hits.bamjunctions.bedinsertions.beddeletions.bed

Agenda




Reporting quantitative expression: FPKM/RPKM

• In NGS RNA-seq experiments, quantitative gene expression data is normalized for total gene/transcript length and the number of sequencing reads, and reported as– RPKM: Reads Per Kilobase of exon per Million mapped

reads. Used for reporting data based on single-end reads– FPKM: Fragments Per Kilobase of exon per Million

fragments. Used for reporting data based on paired-end fragments

Cufflinks to estimate expression

1. Quantify reference genes and transcripts only cufflinks -p 8 -G hg19.gtf -o cuff-tumor accepted_hits.bam

2. Quantify novel genes & transcripts use hg19 as "guide” cufflinks -p 8 -g hg19.gtf -o cuff-tumor accepted_hits.bam

3. Quantify novel genes & transcripts, "unguided" cufflinks -p 8 -o cuff-tumor accepted_hits.bam

Cufflinks (…contd)

• This creates the following files

transcripts.gtf (Generated annotation)

isoforms.fpkm_tracking (Transcript expression)

genes.fpkm_tracking (Gene expression)

skipped.gtf (Skipped annotations)

• Depending on the mode in previous step, these files can have vague identifiers (CUFF*) for gene names if known gene annotations are not used. We have to compare with reference annotations to uncover which genes they are– "Cuffcompare" allows us to do that

Compare assembled transcripts to reference

# Run “cuffcompare” for reference-guided assemblycd cuff2cuffcompare -r ../../hg19.gtf –R -V transcripts.gtf

# This produces the following files in the “cuffcom-out” directory with the following files

cuffcmp.transcripts.gtf.tmap, cuffcmp.transcripts.gtf.refmap, cuffcmp.tracking, cuffcmp.stats, cuffcmp.loci, cuffcmp.combined.gtf

Cuffcompare output

• cuffcmp.stats– Reports statistics related to the "accuracy" of the

transcripts when compared to the reference annotation data.

– Gene finding measures of “sensitivity” and “specificity” are calculated at various levels (nucleotide, exon, intron, transcript, gene)

• cuffcmp.combined.gtf– Reports a GTF file containing the "union" of all transfrags in

each sample

Cuffcompare output

• cuffcmp.loci• cuffcmp.tracking

– Each row contains a transcript structure that is present in one or more input GTF files

Column number

Column name Example Description

1 Cufflinks transfrag id

TCONS_00000045 A unique internal id for the transfrag

2 Cufflinks locus id XLOC_000023 A unique internal id for the locus

3 Reference gene id

Tcea The gene_name attribute of the reference GTF record for this transcript, or '-' if no reference transcript overlaps this Cufflinks transcript

4 Reference transcript id

uc007afj.1 The transcript_id attribute of the reference GTF record for this transcript, or '-' if no reference transcript overlaps this Cufflinks transcript

5 Class code c The type of match between the Cufflinks transcripts in column 6 and the reference transcript. See class codes

http://cufflinks.cbcb.umd.edu/manual.html#class_codes

Cuffcompare output

• cuffcmp.transcripts.gtf.refmap– For each input GTF file, it lists the reference

transcripts, one row per reference transcript for cufflinks transcripts that either fully or partially match it

Column number

Column name

Example Description

1 Reference gene name

uc007crl.1 The gene_name attribute of the reference GTF record for this transcript, if present. Otherwise gene_id is used.

2 Reference transcript id

uc007crl.1 The transcript_id attribute of the reference GTF record for this transcript

3 Class code c The type of match between the Cufflinks transcripts in column 4 and the reference transcript. One of either 'c' for partial match, or '=' for full match.

4 Cufflinks matches

CUFF.23567.0,CUFF.24689.0

A comma separated list of Cufflinks transcript ids matching the reference transcript

Cufflinks output (…contd)

• cuffcmp.transcripts.gtf.tmap – For each input GTF file, it lists the most closely

matching reference transcript, one row per cufflinks transcript, for each cufflinks transcript

– (see column definitions on next slide)

Cufflinks output (…contd)Col. Column name Example Description1 Reference gene name Myog The gene_name attribute of the reference GTF record for this

transcript, if present. Otherwise gene_id is used.

2 Reference transcript id uc007crl.1 The transcript_id attribute of the reference GTF record for this transcript

3 Class code c The type of relationship between the Cufflinks transcripts in column 4 and the reference transcript (see Class Codes)

4 Cufflinks gene id CUFF.23567 The Cufflinks internal gene id5 Cufflinks transcript id CUFF.23567.0 The Cufflinks internal transcript id6 Fraction of major

isoform (FMI)100 The expression of this transcript expressed as a fraction of the

major isoform for the gene. Ranges from 1 to 100.

7 FPKM 1.4567 The expression of this transcript expressed in FPKM8 FPKM_conf_lo 0.7778 The lower limit of the 95% FPKM confidence interval9 FPKM_conf_hi 1.9776 The upper limit of the 95% FPKM confidence interval10 Coverage 3.2687 The estimated average depth of read coverage across the transcript.11 Length 1426 The length of the transcript12 Major isoform ID CUFF.23567.0 The Cufflinks ID of the gene's major isoform

Cuffcompare output (…contd)

• Class codesPriority Code Description1 = Complete match of intron chain2 c Contained3 j Potentially novel isoform (fragment): at least one splice junction is shared with a reference transcript4 e Single exon transfrag overlapping a reference exon and at least 10 bp of a reference intron, indicating

a possible pre-mRNA fragment.5 i A transfrag falling entirely within a reference intron6 o Generic exonic overlap with a reference transcript7 p Possible polymerase run-on fragment (within 2Kbases of a reference transcript)

8 r Repeat. Currently determined by looking at the soft-masked reference sequence and applied to transcripts where at least 50% of the bases are lower case

9 u Unknown, intergenic transcript10 x Exonic overlap with reference on the opposite strand11 s An intron of the transfrag overlaps a reference intron on the opposite strand (likely due to read

mapping errors)12 . (.tracking file only, indicates multiple classifications)

Further analysis

• The slides so far show how to interpret the RNA-seq results of one sample/library

• In an actual experiment, tumor-normal pair might be available as separate library. Refer to tophat-cufflinks diagram to choose workflow– Might involve cuffmerge prior to cuffcompare– Cuffdiff and cuffquant can be used to report

expression or even perform differential expression analysis


Q&A

• Don’t be shy – everyone has a different work flow and we want to help you


RNA-seq for Transcriptome profiling and discovery of novel transcripts and alternatively spliced variants using HPC Presented by: Al Ritacco, Shailender.

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