【正文】 gggggfegcfegggegggfgac[aced`bd__\_c[[Yb Illumina format encodes a Phred quality score from 0 to 40 using ASCII 64 to 104 error probability (p): for solexa: p = , Q = 19。 p = 0,05, Q = , p = , Q = 。 for phred: p = , Q = 20。 p = 0,05, Q = 13, p = , Q = 10。 Data assessment I – Read quality distribution Low Quality ? High Quality ? Trim: 3’ end trim if QN 20 ? Filter: Percent (hight quality Q 30) 60 ? Assessment: Distance Distrubition between two Low quality (Q20) 454 dinucleotide proportion check 454 raw reads quality Data assessment II – Library insert size Numbers of reads with noninsert DNA (full length adapter) in different insert size libraries Data assessment III – Mapping Rate Solexa Sequencing Data Usage in 500bp Library Data assessment IV – Duplication assessment Duplicates detection and filter F R N N 2N Qaverage 20 ? Lane data usage in different solexa library Fiter duplication reads Average Reads per StartPoint Read Correction Correct Illumina GA short reads Kmer = 17 Genome Size Prediction: M = N * ( LK+1)/L N = Total Length (bp) /Genome size L= Average Rads Length (bp) M Genome size estimation using Kmer Before estimating the genome size, we set a hypothesis: the kmer we picked out from the genome can ergodic the whole genome to the Lander waterman algorithm, the algorithm should be represented as: G= Knum / Kdepth Here, G is the genome size, Knum is the total number of kmer and Kdepth is the expected depth of the kmer. If we obtain the expected depth of kmer, we can calculate the genome size. Because the distribution of kmer frequency yields to Poisson distribution, we can consider the peak of the kmer distribution curve as the expected depth of kmer and calculate the genome size. Note: A total of 15,437,084,746 Kmers, the peak value on the right figure is 8, so the genome size is estimated as: 15,437,084,746/8= High Quality Read Rate after preprocess Assembly: Raw data VS preprocessed Data ? Questions ? Genome size estimation methods (Kmer amp。 Cov) ? Assembly optimization (parameters) ? Assembly evaluation (454_Solexa EST) ? Unmappable solexa reads reuse (filterassemble) ? Scaffolding parison (ABI amp。 BIG amp。 Bambus amp。 blat) solexa to solid feasible? ? Assembly assessment (BAC, 3730, necessary ?) Sequencing Strategy for solexa I. Sample preper II. Fragment or Paired End or Mate Pair III. Sequencing different libraries: Data coverage (=500bp) , Data300/Data500=? Data coverage (500bp). 10X, 20X….. Larger genome size, Larger libraries needed. 10K? IV. Length of solexa Reads? 100bp ? F+R=One Reads? V. Other Data: 3730, 454, solid. EST. OVERVIEW OF TESTED ASSEMBLERS Depth VS Coverage EST based Scaffolding 基因組混合拼接驗證及結(jié)構(gòu)變異檢測流程 重復(fù)序列注釋流程 Repeat analysis Lib5 ?Total length: 167,786,201 bp ?Bases masked: 1,267,118 bp % ?SINEs: 298 28,108 ? ALUs 0 0 ? MIRs 211 21,533 ?LINEs: 1,891 347,604 ? LINE1 1,246 292,428 ? LINE2 236 26,166 ? L3/CR1 289 20,348 ?LTR elements: 185 42,779 ? ERVL 39 8,334 ? ERVLMaLRs 55 10,011 ? ERVL_classI 50 12,852 ? ERVL_classII 7 1,292 ?DNA elements: 141 16,729 ? hATCharlie 76 8,642 ? TcMarCharlie 25 3,424 ?Unclassified: 1 139 ?Total interspersed repeats: 435,359 ?Small RNA: 709 101,246 ?Satellites: 2 280 ?Simple repeats: 9,466 560,018 ?Low plexity: 3,674 170,215 基因結(jié)構(gòu)及功能注釋技術(shù)路線 Gene prediction ? De novo prediction – GenScan 16,6093,775 uniprot hit – Augustus 1937810,245 hit ? Homologybased prediction – alignmentgene scaffoldgenewise ? Reference gene set tRNA scan CpG island miRNA prediction Using miRNA database fasta as query and blast with our masked scaffolds Gene function annotation ? Gene Ontology (local uniprot database) ? KEGG (online) GO annotation GenScan uniprot annotation Gene Ontology KEGG pathway overview 血吸蟲 基因家族進(jìn)化分析及比較生物學(xué)分析技術(shù)路線 以應(yīng)用為主導(dǎo)的基因組學(xué)將闊步走向未來 ?走向人類的健康與生活 ?走向人類賴以生存的物質(zhì)基礎(chǔ) ?走向人類賴以生存的環(huán)境 ?走上人類社會和經(jīng)濟(jì)發(fā)展的大舞臺 基因組學(xué)研究成果將走近人類的健康與生活 ? 疾病相關(guān)基因的發(fā)現(xiàn)、功能的鑒定和分子機制的探討 ? 突破常見?。◤?fù)雜疾?。┗蛩降难芯? ? 以基因為基礎(chǔ)的疾病診斷、預(yù)測和預(yù)防 ? 基因治療與細(xì)胞治療治療的結(jié)合 ? 以基因多態(tài)性為基礎(chǔ)的 “ 個體化 ” 藥物 ? 以基因多態(tài)性為基礎(chǔ)的 “ 個體健康計劃 ” ? 傳統(tǒng)藥物、生物藥物和 “ 有機藥物 ” 的自然回歸 走向人類賴以生存的物質(zhì)基礎(chǔ) ?抗病、抗蟲和抗極端環(huán)境 GM農(nóng)作物 ?高生殖率、高生長率、高營養(yǎng)率的GM家畜、家禽和水產(chǎn)品新品種 ?維生素和營養(yǎng)物質(zhì)富集的水果和蔬菜 ?生物殺蟲劑、除草劑和抗病藥物 ?微生態(tài)環(huán)境下生產(chǎn)的有機食品 走向人類賴以生存的環(huán)境 ? 基因組信息記錄了物種億萬年來在環(huán)境變遷中起源和進(jìn)化的歷史。 ? 生物多樣性資源的研究、保護(hù)與開發(fā)：地球上估計有 1億個物種 ? 生態(tài)環(huán)境的研究、保護(hù)與開發(fā)： – 巨大的海洋（占地球總面積 71％） – 廣袤的森林（占地球總面積 40％） – 諸多的湖泊與河流 謝謝！