BAM Alignment File — Output: Alignment Counts and RPKM Expression Measurements for Each Exon • Calculate Coverage Profiles Across the Genome with “Sam2wig”

Total Page:16

File Type:pdf, Size:1020Kb

BAM Alignment File — Output: Alignment Counts and RPKM Expression Measurements for Each Exon • Calculate Coverage Profiles Across the Genome with “Sam2wig” 01 Agenda Item 02 Agenda Item 03 Agenda Item SOLiD TM Bioinformatics Overview (I) July 2010 1 Secondary and Tertiary Primary Analysis Analysis BioScope in cluster SETS ICS Export BioScope in cloud •Satay Plots •Auto Correlation •Heat maps On Instrument Other mapping tools 2 Auto-Export (cycle-by-cycle) Collect Primary Analysis Generate Secondary Images (colorcalls) Csfasta & qual Analysis Instrument Cluster Auto Export delta spch run definition Merge Generate Tertiary Spch files Csfasta & qual Analysis Bioscope Cluster 3 Manual Export Collect Primary Analysis Generate Secondary Images (colorcalls) Csfasta & qual Analysis Instrument Cluster Option in SETS to export All run data or combination of Spch, csfasta & qual files Tertiary Analysis Bioscope Cluster 4 Auto-Export database requirement removed Instrument Cluster Remote Cluster JMS Broker JMS Broker Export ICS Hades Bioscope (ActiveMQ) (ActiveMQ) Daemon Postgres Postgres SETS Bioscope UI Tomcat Tomcat Disco installer Auto-export installer Bioscope installer System installer 5 SOLiDSOLiD DataData AnalysisAnalysis Workflow:Workflow: Secondary/Tertiary Analysis (off -instrument) Primary Analysis Secondary Analysis Tertiary Analysis BioScope BioScope Reseq WT SAET •SNP •Coverage .csfasta / .qual Accuracy Enhanced .csfasta •InDel •Exon Counting •CNV •Junction Finder •Inversion •Fusion Transcripts Mapping Mapped reads (.ma) Third Party Tools Mapped reads (.bam) maToBam 6 01 Agenda Item 02 Agenda Item 03 Agenda Item SOLiD TM Bioinformatics Overview (II) July 2010 7 OutlineOutline • Color space and 2-base-encoding • Quality values and filtering • Mapping algorithm and considerations • SOLiD Webinar and Online Training • SOLiD Software Community 8 WhatWhat IsIs ColorColor Space?Space? • Capillary electrophoresis uses single base, color encoding of data Collect color Identify peak Convert to Identify peaks image colors base calls Base space Color space 9 SOLiDSOLiD ColorColor SpaceSpace • SOLiD uses 2 base color encoding of data (2BE) Collect color Identify bead Identify beads image color Record colors for each bead over consecutive cycles Color space Base space A C G G T C G T C G T G T G C G T 10 PropertiesProperties OfOf 22 --BaseBase EncodingEncoding (2BE)(2BE) Second Base 5’ 3’ 1 3 1 3 1 3 2 3 5’-A C G T A C G A T -3’ 3’-T G C A T G C T A -5’ 1 3 1 3 1 3 2 3 Base First 3’ 5’ • Two dibases that agree in just one base have different colors — color(AC) ≠ color(AG) ≠ color(AT) ≠ color(AA) • Two dibases that do not agree in either base have same color — color(AC) = color(GT) and color(CG) = color(AT) • A dibase and its reverse have the same color — color(AC) = color(CA), color(GT) = color(TG) • Repeated-base dibases have the same color — color(AA) = color(CC)= color(GG)= color(TT) 11 ““ValidValid ”” andand ““InvalidInvalid ”” AdjacentAdjacent ColorColor SubstitutionsSubstitutions • “Invalid” changes are inconsistent with SNP and likely sequencing errors 12 OutlineOutline • Color space and 2-base-encoding • Quality values and filtering • Mapping algorithm and considerations • SOLiD Webinar and Online Training • SOLiD Software Community 13 QualityQuality ValueValue (QV)(QV) ForFor ColorColor CallCall • A score calculated based on the probability of an error call at that base • Similar to those generated by phred and the KB Basecaller for capillary electrophoresis sequencing = − q 10 log 10 p p = probability of color call error • A QV score of 10 represent 10% error rate, whereas a QV score of 20 represents a 1% error rate 14 SETS Software Updates What has changed in SETS? Primary Analysis Filtering enabled - Removes poor quality beads, primary analysis results file size will be reduced. Mapping will be performed faster and matching % will improve. Filter Poor quality Beads Mappable Beads 15 Why Filter? • By removing poor quality beads, primary analysis results would be reduced by about 15% or more • Easier to discover novel information from remaining unmatched beads • Due to smaller list of reads of a run, mapping would be faster for generating similar throughput • Improved matching percentage 16 Filtering Design • Used Human data as training set • Set parameters based on the number of poor quality beads filtered — 20 value corresponds to 20% of poor quality beads filtered out — 80 value corresponds to 80% of poor quality beads filtered out • Tested mapping using BioScope Classic mapping 17 Configuring Filtering from SETS •Valid ranges for Stringency are from 0 to 80 •Default value is 20 20 18 OutlineOutline • Color space and 2-base-encoding • Quality values and filtering • Mapping algorithm and considerations • SOLiD Webinar and Online Training • SOLiD Software Community 19 MappingMapping AlgorithmAlgorithm • Challenge: — A small word size is needed for continuous word searches in short reads. This is computationally and time intensive. • Our Approach: — Use discontinuous word patterns > Allows faster searching and guaranteed to find all hits up to a certain number of mismatches 20 DiscontinuousDiscontinuous WordsWords • Continuous words: searching for a perfect alignment, 8/8 bases (word size 8, e.g. used by BLAST) ATTTTTT GGGTAGCC CCTTGGATGAGT |||||||| AG GGGTAGCC TGATGATGGT • Discontinuous words: searching 8/18 matches (effective word size is also 8) ATTTT TT GGGTA GC CCCTT GGAT GAGT || || |||| TT GACCG GC ATGGG GGAT 110000011000001111 21 MappingMapping ToolTool -- mapreadsmapreads • General features of mapping tool — Aligns in color space — Translates reference sequence to color space — Allows mismatches (no indels), valid adjacent mismatches can be counted as one — Allows masking of certain positions (bad calls) — For fixed reference sequence, running time is linear with number of reads • New with SOLiD 3+ — Seed and extend mapping approach — Multi-threaded 22 LocalLocal MappingMapping • Motivation — Long reads, non-uniform quality — At the end of reads errors tend to accumulate — Some applications show sequencing into adaptors 23 LocalLocal AlignmentAlignment StrategyStrategy • Map the first 25 colors of the read to allowing 2 mismatches (MM). • For every hit found (up to the Z-limit), do a local extension — Accumulate alignment score (Match = 1, MM = -2 [user defined] ) — Report the best partial alignment (anchored local) based on score > Discard if score does not meet minimum cutoff Read: 0122130123012303201203021 123012310231203120103120 ||||||| ||||||||||||||| | ||||| |||||||||| ||| Ref: 0122130 0230123032012030 1112301 13102312031 0010 1203 • For reads not mapped, shift anchor location and attempt additional mapping 24 LocalLocal Mapping:Mapping: AnchorAnchor OffsetOffset start end reference read (offset) • start and end mark the start and end of the alignment in the reference. • The alignment may not encompass the entire read. • The start of the alignment in the read is called the offset 25 MappingMapping QV:QV: MathematicalMathematical DefinitionDefinition • Mapping quality is an estimate how likely an alignment is correct • First, calculate the posterior probability L−t − 1 P(r | Alignment ) = 1( − e)t m e m 4 • If an alignment has a probability of ,P(r), it’s mapping QV is defined as P(r) — -10*log 10 (1-P(r)/P), where P = Σ for all reads 26 What are mapping/pairing quality values? • Given the fact that a read R of length L can map to n different locations Xi (i = 1…n) in the genome, mapping quality value represents the probability of the hypothesis, that the read maps to location Xi is true. Mapping Quality value ~ Prob hypothesis (R Ξ X1 | R) is true R X1 X2 Xn 27 Difference between Mapping QV & Pairing QV • Mapping QV represents the quality of alignment for Fragment reads or the quality of alignment for individual tags (F3/R3/F5-P2) in pared reads • Pairing QV represents the quality of alignment for a pair of reads. Example if F3 tag has 10 alignments and F5-P2 tag has 10 alignments, then we could form 100 alignment pairs for tags F3, F5-P2 together 28 Parameters that factor into Pairing Quality Values • Alignment Length • Number of mismatches • Offset • Insert size •Total number of possible alignments Offset Alignment Length - R3/F5 Insert Size F3 + 29 Phred quality score and Pairing Quality Values Phred Quality score used most commonly used in literature is -10 x log 10 [prob (error)]. So to be consistent with Phred scaled quality score, we calculate the pairing quality value (PQV) as: =− × [ − ( )] PQV 10 log 10 1 Q r1,r2,x1,x2 Finally, we normalize the PQV with the maximum possible PQV for a given pair of reads of read length L1 & L2, to keep the PQVs in the range of 1 – 100 PQV PQV = ×100 PQV max 30 MultithreadedMultithreaded MapreadsMapreads • Single mapping job • Fraction of reads (1/n) are mapped against the whole reference • ~20GB of RAM for the human genome • Limit read mapping to whole genome (-z) • Combine results (simple merge) 1/n reads Mapped CPU 1 (.csfasta) Results 1 1/n reads Full Mapped Combined CPU 2 (.csfasta) Reference Results 2 Results . 1/n reads Mapped CPU n (.csfasta) Results n 31 LocalLocal Mapping:Mapping: AdvantagesAdvantages • Increased throughput — Some data sets have observed 2-fold increase in mapping using local mapping vs. classical mapping • Increased speed — Up to 15X Faster than iterative mapping with trimming • As read length increases, only a small set of schemas is needed to be optimized 32 OutlineOutline • Color space and 2-base-encoding • Quality values and filtering • Mapping algorithm and considerations • SOLID Webinar and Online Training • SOLiD Software Community 33 Introducing 34 SOLiD™ University Offerings
Recommended publications
  • Mouse Kcnip2 Conditional Knockout Project (CRISPR/Cas9)
    https://www.alphaknockout.com Mouse Kcnip2 Conditional Knockout Project (CRISPR/Cas9) Objective: To create a Kcnip2 conditional knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Kcnip2 gene (NCBI Reference Sequence: NM_145703 ; Ensembl: ENSMUSG00000025221 ) is located on Mouse chromosome 19. 10 exons are identified, with the ATG start codon in exon 1 and the TAG stop codon in exon 10 (Transcript: ENSMUST00000162528). Exon 4 will be selected as conditional knockout region (cKO region). Deletion of this region should result in the loss of function of the Mouse Kcnip2 gene. To engineer the targeting vector, homologous arms and cKO region will be generated by PCR using BAC clone RP23-98F2 as template. Cas9, gRNA and targeting vector will be co-injected into fertilized eggs for cKO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Mice homozygous for disruptions in this gene are susceptible to induced cardiac arrhythmias but are otherwise normal. Exon 4 starts from about 27.65% of the coding region. The knockout of Exon 4 will result in frameshift of the gene. The size of intron 3 for 5'-loxP site insertion: 574 bp, and the size of intron 4 for 3'-loxP site insertion: 532 bp. The size of effective cKO region: ~625 bp. The cKO region does not have any other known gene. Page 1 of 8 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele gRNA region 5' gRNA region 3' 1 2 3 4 5 6 7 8 9 10 Targeting vector Targeted allele Constitutive KO allele (After Cre recombination) Legends Exon of mouse Kcnip2 Homology arm cKO region loxP site Page 2 of 8 https://www.alphaknockout.com Overview of the Dot Plot Window size: 10 bp Forward Reverse Complement Sequence 12 Note: The sequence of homologous arms and cKO region is aligned with itself to determine if there are tandem repeats.
    [Show full text]
  • Gene Discovery and Annotation Using LCM-454 Transcriptome Sequencing Scott J
    Downloaded from genome.cshlp.org on September 23, 2021 - Published by Cold Spring Harbor Laboratory Press Methods Gene discovery and annotation using LCM-454 transcriptome sequencing Scott J. Emrich,1,2,6 W. Brad Barbazuk,3,6 Li Li,4 and Patrick S. Schnable1,4,5,7 1Bioinformatics and Computational Biology Graduate Program, Iowa State University, Ames, Iowa 50010, USA; 2Department of Electrical and Computer Engineering, Iowa State University, Ames, Iowa 50010, USA; 3Donald Danforth Plant Science Center, St. Louis, Missouri 63132, USA; 4Interdepartmental Plant Physiology Graduate Major and Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, Iowa 50010, USA; 5Department of Agronomy and Center for Plant Genomics, Iowa State University, Ames, Iowa 50010, USA 454 DNA sequencing technology achieves significant throughput relative to traditional approaches. More than 261,000 ESTs were generated by 454 Life Sciences from cDNA isolated using laser capture microdissection (LCM) from the developmentally important shoot apical meristem (SAM) of maize (Zea mays L.). This single sequencing run annotated >25,000 maize genomic sequences and also captured ∼400 expressed transcripts for which homologous sequences have not yet been identified in other species. Approximately 70% of the ESTs generated in this study had not been captured during a previous EST project conducted using a cDNA library constructed from hand-dissected apex tissue that is highly enriched for SAMs. In addition, at least 30% of the 454-ESTs do not align to any of the ∼648,000 extant maize ESTs using conservative alignment criteria. These results indicate that the combination of LCM and the deep sequencing possible with 454 technology enriches for SAM transcripts not present in current EST collections.
    [Show full text]
  • BIO4342 Exercise 2: Browser-Based Annotation and RNA-Seq Data
    BIO4342 Exercise 2: Browser-Based Annotation and RNA-Seq Data Jeremy Buhler March 15, 2010 This exercise continues your introduction to practical issues in comparative annotation. You’ll be annotating genomic sequence from the dot chromosome of Drosophila mojavensis using your knowledge of BLAST and some improved visualization tools. You’ll also consider how best to integrate information from high-throughput sequencing of expressed RNA. 1 Getting Started To begin, go to our local genome browser at http://gander.wustl.edu/. Select “Genome Browser” from the left-side menu and choose the “Improved Dot” assembly of D. mojavensis for viewing. Finally, hit submit to start looking at the sequence. The entire dot assembly is about 1.69 megabases in length; zoom out to see everything. This assembly is built from a set of overlapping fosmid clones prepared for the 2009 edition of BIO 4342. We’ve added a variety of information to the genome browser to help you annotate, such as: • gene-structure predictions from several different tools; • repeats annotated using the RepeatMasker program; • BLAST hits to D. melanogaster proteins; • RNA-Seq data, which we’ll describe in more detail later. Having all this evidence available at once is somewhat overwhelming. To keep the view to a manageable level, I’d suggest that you initially set all the gene prediction tracks (Genscan, Nscan, SNAP, Geneid), as well as the repeat tracks, to “dense” mode, so that each displays on a single line. Set the BLAST hit track (called “D. mel proteins”) to “pack” to see the locations of all BLAST hits, and set the “RNA-Seq Coverage” track to “full” and the “TopHat junctions” track to “pack” to get a detailed view of these results.
    [Show full text]
  • BLAT—The BLAST-Like Alignment Tool
    Resource BLAT—The BLAST-Like Alignment Tool W. James Kent Department of Biology and Center for Molecular Biology of RNA, University of California, Santa Cruz, Santa Cruz, California 95064, USA Analyzing vertebrate genomes requires rapid mRNA/DNA and cross-species protein alignments. A new tool, BLAT, is more accurate and 500 times faster than popular existing tools for mRNA/DNA alignments and 50 times faster for protein alignments at sensitivity settings typically used when comparing vertebrate sequences. BLAT’s speed stems from an index of all nonoverlapping K-mers in the genome. This index fits inside the RAM of inexpensive computers, and need only be computed once for each genome assembly. BLAT has several major stages. It uses the index to find regions in the genome likely to be homologous to the query sequence. It performs an alignment between homologous regions. It stitches together these aligned regions (often exons) into larger alignments (typically genes). Finally, BLAT revisits small internal exons possibly missed at the first stage and adjusts large gap boundaries that have canonical splice sites where feasible. This paper describes how BLAT was optimized. Effects on speed and sensitivity are explored for various K-mer sizes, mismatch schemes, and number of required index matches. BLAT is compared with other alignment programs on various test sets and then used in several genome-wide applications. http://genome.ucsc.edu hosts a web-based BLAT server for the human genome. Some might wonder why in the year 2002 the world needs sions on any number of perfect or near-perfect hits. Where another sequence alignment tool.
    [Show full text]
  • EMBL-EBI Powerpoint Presentation
    Processing data from high-throughput sequencing experiments Simon Anders Use-cases for HTS · de-novo sequencing and assembly of small genomes · transcriptome analysis (RNA-Seq, sRNA-Seq, ...) • identifying transcripted regions • expression profiling · Resequencing to find genetic polymorphisms: • SNPs, micro-indels • CNVs · ChIP-Seq, nucleosome positions, etc. · DNA methylation studies (after bisulfite treatment) · environmental sampling (metagenomics) · reading bar codes Use cases for HTS: Bioinformatics challenges Established procedures may not be suitable. New algorithms are required for · assembly · alignment · statistical tests (counting statistics) · visualization · segmentation · ... Where does Bioconductor come in? Several steps: · Processing of the images and determining of the read sequencest • typically done by core facility with software from the manufacturer of the sequencing machine · Aligning the reads to a reference genome (or assembling the reads into a new genome) • Done with community-developed stand-alone tools. · Downstream statistical analyis. • Write your own scripts with the help of Bioconductor infrastructure. Solexa standard workflow SolexaPipeline · "Firecrest": Identifying clusters ⇨ typically 15..20 mio good clusters per lane · "Bustard": Base calling ⇨ sequence for each cluster, with Phred-like scores · "Eland": Aligning to reference Firecrest output Large tab-separated text files with one row per identified cluster, specifying · lane index and tile index · x and y coordinates of cluster on tile · for each
    [Show full text]
  • An Open-Sourced Bioinformatic Pipeline for the Processing of Next-Generation Sequencing Derived Nucleotide Reads
    bioRxiv preprint doi: https://doi.org/10.1101/2020.04.20.050369; this version posted May 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. An open-sourced bioinformatic pipeline for the processing of Next-Generation Sequencing derived nucleotide reads: Identification and authentication of ancient metagenomic DNA Thomas C. Collin1, *, Konstantina Drosou2, 3, Jeremiah Daniel O’Riordan4, Tengiz Meshveliani5, Ron Pinhasi6, and Robin N. M. Feeney1 1School of Medicine, University College Dublin, Ireland 2Division of Cell Matrix Biology Regenerative Medicine, University of Manchester, United Kingdom 3Manchester Institute of Biotechnology, School of Earth and Environmental Sciences, University of Manchester, United Kingdom [email protected] 5Institute of Paleobiology and Paleoanthropology, National Museum of Georgia, Tbilisi, Georgia 6Department of Evolutionary Anthropology, University of Vienna, Austria *Corresponding Author Abstract The emerging field of ancient metagenomics adds to these Bioinformatic pipelines optimised for the processing and as- processing complexities with the need for additional steps sessment of metagenomic ancient DNA (aDNA) are needed in the separation and authentication of ancient sequences from modern sequences. Currently, there are few pipelines for studies that do not make use of high yielding DNA cap- available for the analysis of ancient metagenomic DNA ture techniques. These bioinformatic pipelines are tradition- 1 4 ally optimised for broad aDNA purposes, are contingent on (aDNA) ≠ The limited number of bioinformatic pipelines selection biases and are associated with high costs.
    [Show full text]
  • A Multithread Blat Algorithm Speeding up Aligning Sequences to Genomes Meng Wang and Lei Kong*
    Wang and Kong BMC Bioinformatics (2019) 20:28 https://doi.org/10.1186/s12859-019-2597-8 SOFTWARE Open Access pblat: a multithread blat algorithm speeding up aligning sequences to genomes Meng Wang and Lei Kong* Abstract Background: The blat is a widely used sequence alignment tool. It is especially useful for aligning long sequences and gapped mapping, which cannot be performed properly by other fast sequence mappers designed for short reads. However, the blat tool is single threaded and when used to map whole genome or whole transcriptome sequences to reference genomes this program can take days to finish, making it unsuitable for large scale sequencing projects and iterative analysis. Here, we present pblat (parallel blat), a parallelized blat algorithm with multithread and cluster computing support, which functions to rapidly fine map large scale DNA/RNA sequences against genomes. Results: The pblat algorithm takes advantage of modern multicore processors and significantly reduces the run time with the number of threads used. pblat utilizes almost equal amount of memory as when running blat. The results generated by pblat are identical with those generated by blat. The pblat tool is easy to install and can run on Linux and Mac OS systems. In addition, we provide a cluster version of pblat (pblat-cluster) running on computing clusters with MPI support. Conclusion: pblat is open source and free available for non-commercial users. It is easy to install and easy to use. pblat and pblat-cluster would facilitate the high-throughput mapping of large scale genomic and transcript sequences to reference genomes with both high speed and high precision.
    [Show full text]
  • Sequence Alignment/Map Format Specification
    Sequence Alignment/Map Format Specification The SAM/BAM Format Specification Working Group 3 Jun 2021 The master version of this document can be found at https://github.com/samtools/hts-specs. This printing is version 53752fa from that repository, last modified on the date shown above. 1 The SAM Format Specification SAM stands for Sequence Alignment/Map format. It is a TAB-delimited text format consisting of a header section, which is optional, and an alignment section. If present, the header must be prior to the alignments. Header lines start with `@', while alignment lines do not. Each alignment line has 11 mandatory fields for essential alignment information such as mapping position, and variable number of optional fields for flexible or aligner specific information. This specification is for version 1.6 of the SAM and BAM formats. Each SAM and BAMfilemay optionally specify the version being used via the @HD VN tag. For full version history see Appendix B. Unless explicitly specified elsewhere, all fields are encoded using 7-bit US-ASCII 1 in using the POSIX / C locale. Regular expressions listed use the POSIX / IEEE Std 1003.1 extended syntax. 1.1 An example Suppose we have the following alignment with bases in lowercase clipped from the alignment. Read r001/1 and r001/2 constitute a read pair; r003 is a chimeric read; r004 represents a split alignment. Coor 12345678901234 5678901234567890123456789012345 ref AGCATGTTAGATAA**GATAGCTGTGCTAGTAGGCAGTCAGCGCCAT +r001/1 TTAGATAAAGGATA*CTG +r002 aaaAGATAA*GGATA +r003 gcctaAGCTAA +r004 ATAGCT..............TCAGC -r003 ttagctTAGGC -r001/2 CAGCGGCAT The corresponding SAM format is:2 1Charset ANSI X3.4-1968 as defined in RFC1345.
    [Show full text]
  • Homology & Alignment
    Protein Bioinformatics Johns Hopkins Bloomberg School of Public Health 260.655 Thursday, April 1, 2010 Jonathan Pevsner Outline for today 1. Homology and pairwise alignment 2. BLAST 3. Multiple sequence alignment 4. Phylogeny and evolution Learning objectives: homology & alignment 1. You should know the definitions of homologs, orthologs, and paralogs 2. You should know how to determine whether two genes (or proteins) are homologous 3. You should know what a scoring matrix is 4. You should know how alignments are performed 5. You should know how to align two sequences using the BLAST tool at NCBI 1 Pairwise sequence alignment is the most fundamental operation of bioinformatics • It is used to decide if two proteins (or genes) are related structurally or functionally • It is used to identify domains or motifs that are shared between proteins • It is the basis of BLAST searching (next topic) • It is used in the analysis of genomes myoglobin Beta globin (NP_005359) (NP_000509) 2MM1 2HHB Page 49 Pairwise alignment: protein sequences can be more informative than DNA • protein is more informative (20 vs 4 characters); many amino acids share related biophysical properties • codons are degenerate: changes in the third position often do not alter the amino acid that is specified • protein sequences offer a longer “look-back” time • DNA sequences can be translated into protein, and then used in pairwise alignments 2 Find BLAST from the home page of NCBI and select protein BLAST… Page 52 Choose align two or more sequences… Page 52 Enter the two sequences (as accession numbers or in the fasta format) and click BLAST.
    [Show full text]
  • Multi-Scale Analysis and Clustering of Co-Expression Networks
    Multi-scale analysis and clustering of co-expression networks Nuno R. Nen´e∗1 1Department of Genetics, University of Cambridge, Cambridge, UK September 14, 2018 Abstract 1 Introduction The increasing capacity of high-throughput genomic With the advent of technologies allowing the collec- technologies for generating time-course data has tion of time-series in cell biology, the rich structure stimulated a rich debate on the most appropriate of the paths that cells take in expression space be- methods to highlight crucial aspects of data struc- came amenable to processing. Several methodologies ture. In this work, we address the problem of sparse have been crucial to carefully organize the wealth of co-expression network representation of several time- information generated by experiments [1], including course stress responses in Saccharomyces cerevisiae. network-based approaches which constitute an excel- We quantify the information preserved from the origi- lent and flexible option for systems-level understand- nal datasets under a graph-theoretical framework and ing [1,2,3]. The use of graphs for expression anal- evaluate how cross-stress features can be identified. ysis is inherently an attractive proposition for rea- This is performed both from a node and a network sons related to sparsity, which simplifies the cumber- community organization point of view. Cluster anal- some analysis of large datasets, in addition to being ysis, here viewed as a problem of network partition- mathematically convenient (see examples in Fig.1). ing, is achieved under state-of-the-art algorithms re- The properties of co-expression networks might re- lying on the properties of stochastic processes on the veal a myriad of aspects pertaining to the impact of constructed graphs.
    [Show full text]
  • A Dissertation
    A Dissertation entitled Strategies for Membrane Protein Studies and Structural Characterization of a Metabolic Enzyme for Antibiotic Development by Buenafe T. Arachea Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Doctor of Philosophy Degree in Chemistry Dr. Ronald E. Viola, Committee Chair Dr. Max O. Funk, Committee Member Dr. Donald Ronning, Committee Member Dr. Marcia McInerney, Committee Member Dr. Patricia R. Komuniecki, Dean College of Graduate Studies The University of Toledo August 2011 Copyright © 2011, Buenafe T. Arachea This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author. An Abstract of Strategies for Membrane Protein Studies and Structural Characterization of a Metabolic Enzyme for Antibiotic Development by Buenafe T. Arachea Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Doctor of Philosophy Degree in Chemistry The University of Toledo August 2011 Membrane proteins are essential in a variety of cellular functions, making them viable targets for drug development. However, progress in the structural elucidation of membrane proteins has proven to be a difficult task, thus limiting the number of published structures of membrane proteins as compared with the enormous structural information obtained from soluble proteins. The challenge in membrane protein studies lies in the production of the required sample for characterization, as well as in developing methods to effectively solubilize and maintain a functional and stable form of the target protein during the course of crystallization. To address these issues, two different approaches were explored for membrane protein studies.
    [Show full text]
  • NGS Raw Data Analysis
    NGS raw data analysis Introduc3on to Next-Generaon Sequencing (NGS)-data Analysis Laura Riva & Maa Pelizzola Outline 1. Descrip7on of sequencing plaorms 2. Alignments, QC, file formats, data processing, and tools of general use 3. Prac7cal lesson Illumina Sequencing Single base extension with incorporaon of fluorescently labeled nucleodes Library preparaon Automated cluster genera3on DNA fragmenta3on and adapter ligaon Aachment to the flow cell Cluster generaon by bridge amplifica3on of DNA fragments Sequencing Illumina Output Quality Scores Sequence Files Comparison of some exisng plaGorms 454 Ti RocheT Illumina HiSeqTM ABI 5500 (SOLiD) 2000 Amplificaon Emulsion PCR Bridge PCR Emulsion PCR Sequencing Pyrosequencing Reversible Ligaon-based reac7on terminators sequencing Paired ends/sep Yes/3kb Yes/200 bp Yes/3 kb Read length 400 bp 100 bp 75 bp Advantages Short run 7mes. The most popular Good base call Longer reads plaorm accuracy. Good improve mapping in mulplexing repe7ve regions. capability Ability to detect large structural variaons Disadvantages High reagent cost. Higher error rates in repeat sequences The Illumina HiSeq! Stacey Gabriel Libraries, lanes, and flowcells Flowcell Lanes Illumina Each reaction produces a unique Each NGS machine processes a library of DNA fragments for single flowcell containing several sequencing. independent lanes during a single sequencing run Applications of Next-Generation Sequencing Basic data analysis concepts: • Raw data analysis= image processing and base calling • Understanding Fastq • Understanding Quality scores • Quality control and read cleaning • Alignment to the reference genome • Understanding SAM/BAM formats • Samtools • Bedtools Understanding FASTQ file format FASTQ format is a text-based format for storing a biological sequence and its corresponding quality scores.
    [Show full text]