Advancements in Next-Generation Sequencing: A Comprehensive Informatics Perspective
This presentation covers the latest advancements in next-generation sequencing (NGS) technologies from an informatics standpoint. It highlights the unique features of leading systems, including Roche/454 FLX, Illumina/Solexa, AB/SOLiD, and Helicos/HeliScope. The talk emphasizes applications for variation discovery, structural variation detection, novel transcript discovery, and expression profiling. Key considerations for read length, error rates, and biases encountered in sequencing data are discussed, along with the importance of paired-end read protocols for enhancing data accuracy and efficiency in genomic studies.
Advancements in Next-Generation Sequencing: A Comprehensive Informatics Perspective
E N D
Presentation Transcript
Next-generation sequencing – the informatics angle Gabor T. Marth Boston College Biology Department AGBT 2008 Marco Island, FL. February 6. 2008
T1. Roche / 454 FLX system • pyrosequencing technology • variable read-length • the only new technology with >100bp reads • tested in many published applications • supports paired-end read protocols with up to 10kb separation size
T2. Illumina / Solexa Genome Analyzer • fixed-length short-read sequencer • read properties are very close traditional capillary sequences • very low INDEL error rate • tested in many published applications • paired-end read protocols support short (<600bp) separation
T3. AB / SOLiD system 2nd Base A C G T 0 1 2 3 A 1 0 3 2 1st Base C 2 3 0 1 G 1 3 2 0 T • fixed-length short-read sequencer • employs a 2-base encoding system that can be used for error reduction and improving SNP calling accuracy • requires color-space informatics • published applications underway / in review • paired-end read protocols support up to 10kb separation size
T4. Helicos / Heliscope system • experimental short-read sequencer system • single molecule sequencing • no amplification • variable read-length • error rate reduced with 2-pass template sequencing
A1. Variation discovery: SNPs and short-INDELs 1. sequence alignment 2. dealing with non-unique mapping 3. looking for allelic differences
A2. Structural variation detection • copy number (for amplifications, deletions) from depth of read coverage • structural variations (deletions, insertions, inversions and translocations) from paired-end read map locations
A3. Identification of protein-bound DNA genome sequence aligned reads Chromatin structure (CHIP-SEQ) (Mikkelsen et al. Nature 2007) Transcription binding sites. Robertson et al. Nature Methods, 2007
A4. Novel transcript discovery (genes) Known exon 1 Known exon 2 • novel transcripts in known genes Known exon 1 Known exon 2 • novel genes / exons Inferred exon 1 Inferred exon 2
A5. Novel transcript discovery (miRNAs) Ruby et al. Cell, 2006
A6. Expression profiling by tag counting gene gene aligned reads aligned reads Jones-Rhoads et al. PLoS Genetics, 2007
A7. De novo organismal genome sequencing Lander et al. Nature 2001 short reads read pairs longer reads assembled sequence contigs
C1. Read length 20-35 (var) 25-35 (fixed) 25-40 (fixed) ~250 (var) 100 200 300 0 read length [bp]
When does read length matter? • longer reads are needed where one must use parts of reads for mapping: • de novo sequencing • novel transcript discovery aacttagacttaca gacttacatacgta Known exon 1 Known exon 2 accgattactatacta • short reads often sufficient where the entire read length can be used for mapping: • SNPs, short-INDELs, SVs • CHIP-SEQ • short RNA discovery • counting (mRNA miRNA)
C2. Read error rate • error rate dictates how many errors the aligner should tolerate • the more errors the aligner must tolerate, the lower the fraction of the reads that can be uniquely aligned • applications where, in addition, specific alleles are essential, error rate is even more important • error rate typically 0.4 - 1%
C3. Error rate grows with each cycle • this phenomenon limits useful read length
C4. Substitutions vs. INDEL errors • gapped alignment necessary • good SNP discovery accuracy • short-INDEL discovery difficult • SNP discovery may require higher coverage for allele confirmation • INDELs can be discovered with very high confidence!
C5. Quality values are important for allele calling • inaccurate or not well calibrated base quality values hinder allele calling Q-values should be accurate … and high! • PHRED base quality values represent the estimated likelihood of sequencing error and help us pick out true alternate alleles
Quality values should be well-calibrated assigned base quality value should be calibrated to represent the actual base quality value in every sequencing cycle
C6. Representational biases / library complexity fragmentation biases PCR amplification biases sequencing low/no representation sequencing biases high representation
Dispersal of read coverage • this affects variation discovery (deeper starting read coverage is needed) • it has major impact is on counting applications
Amplification errors early amplification error gets propagated onto every clonal copy many reads from clonal copies of a single fragment • early PCR errors in “clonal” read copies lead to false positive allele calls
C7. Paired-end reads • circularization: 500bp - 10kb (sweet spot ~3kb) • fragment length limited by library complexity Korbel et al. Science 2007 • fragment amplification: fragment length 100 - 600 bp • fragment length limited by amplification efficiency • paired-end read can improve read mapping accuracy (if unique map positions are required for both ends) or efficiency (if fragment length constraint is used to rescue non-uniquely mapping ends)
Paired-end reads for SV discovery • longer fragments tend to have wider fragment length distributions • SV breakpoint detection sensitivity & resolution depend on the width of the fragment length distribution (most 2kb deletions would be detected at 10% std but missed at 30% std) • longer fragments increase the chance of spanning SV breakpoints and/or entire events
Thanks Michael Stromberg MOSAIK talk Thursday, 7:40PM Chip Stewart Michele Busby Aaron Quinlan Damien Croteau-Chonka Eric Tsung Derek Barnett Weichun Huang http://bioinformatics.bc.edu/marthlab Michael Egholm David Bentley Francisco de la Vega Kristen Stoops Ed Thayer Clive Brown Elaine Mardis