原文：http://blog.sina.com.cn/s/blog_751bd9440102v72b.html

一. 簡介

Cufflinks下主要包含cufflinks,cuffmerge,cuffcompare和cuffdiff等幾支主要的程序。主要用于基因表達(dá)量的計(jì)算和差異表達(dá)基因的尋找扒怖。

二. 安裝

Cufflinks下載網(wǎng)頁练对。

1. 為了安裝Cufflinks，必須有Boost C++

libraries蕴忆。下載Boost并安裝参萄。默認(rèn)安裝在/usr/local驮履。

$ tar jxvf boost_1_53_0.tar.bz2

$ cd boost_1_53_0

$ ./bootstrap.sh

$ sudo ./b2 install

2.安裝SAM tools苦锨。

下載SAM tools。$ tar jxvf samtools-0.1.18.tar.bz2$ cd samtools-0.1.18$ make$ sudo su # mkdir /usr/local/include/bam# cp libbam.a /usr/local/lib# cp *.h /usr/local/include/bam/# cp samtools /usr/bin/

3. 安裝 Eigen libraries药磺。

下載Eigen$ tar jxvf 3.1.2.tar.bz2$ cd eigen-eigen-5097c01bcdc4$ sudo cp -r Eigen/ /usr/local/include/

4. 安裝Cufflinks告组。

$ tar zxvf cufflinks-2.0.2.tar.gz

$ cd cufflinks-2.0.2

$ ./configure --prefix=/path/to/cufflinks/install --with-boost=/usr/local/ --with-eigen=/usr/local/include//Eigen/

$ make

$ make install

5. 可以直接下載Linux x86_64 binary。不需要上述繁瑣步驟癌佩，解壓后的程序直接可用木缝。(推薦)

三.Cufflinks的使用

1. Cufflinks簡介

Cufflinks程序主要根據(jù)Tophat的比對(duì)結(jié)果，依托或不依托于參考基因組的GTF注釋文件围辙，計(jì)算出(各個(gè)gene的)isoform的FPKM值我碟，并給出trascripts.gtf注釋結(jié)果(組裝出轉(zhuǎn)錄組)。

注意：

1.

fragment的長度的估測(cè)姚建，若為pair-end測(cè)序矫俺，則cufflinks自己會(huì)有一套算法，算出結(jié)果掸冤。若為single-end測(cè)序厘托，則cufflinks默認(rèn)的是高斯分布，或者你自己提供相關(guān)的參數(shù)設(shè)置稿湿。

2. cufflinks計(jì)算multi-mapped reads铅匹，一般a read map到10個(gè)位置，則每個(gè)位置記為10%饺藤。a

read mapping to 10 positions will count as 10% of a read at each

position.

3. 一般不推薦用cufflinks拼接細(xì)菌的轉(zhuǎn)錄組包斑，推薦Glimmer。但是涕俗，若有注釋文件罗丰，可以用cufflinks和cuffdiff來檢測(cè)基因的表達(dá)和差異性。

4. cufflinks/cuffdiff不能計(jì)算出exon或splicing event的FPKM

5.cuffdiff處理時(shí)間序列data：采用參數(shù)-t

6.當(dāng)你使用cufflinks時(shí)咽袜，在最后出現(xiàn)了99%丸卷，然后一直不動(dòng)。因?yàn)閏uffdiff需要更多的CPU來處理一些匹配很多reads的loci询刹。而這些位點(diǎn)一般要等其他位點(diǎn)全部解決了后谜嫉，才由cuffdiff來處理“剂可以用參數(shù)-M來提供相關(guān)的文件沐兰，過濾掉rRNA或者線粒體RNA。

7. 當(dāng)使用cufflinks或cuffdiff出現(xiàn)了“crash with a ‘bad_alloc'

error”蔽挠，cuffdiff和cufflinks運(yùn)行了很長時(shí)間才結(jié)束————這表明計(jì)算機(jī)拼接一個(gè)高表達(dá)的基因或定量分析一個(gè)高表達(dá)的基因住闯，運(yùn)行的內(nèi)存使用玩盡了瓜浸！解決方法：修改選項(xiàng)“-max-bundle-frags”，可以先嘗試500000比原，若錯(cuò)誤依舊在插佛，可以繼續(xù)下調(diào)！

8. cuffdiff報(bào)道的結(jié)果里面所有的基因和轉(zhuǎn)錄本的FPKM=0量窘，這表明GTF中的染色體名字和BAM里的名字不匹配雇寇。

9.cuffdiff和cufflinks的缺點(diǎn)：存在一定的假基因和轉(zhuǎn)錄本（原因：測(cè)序深度，測(cè)序質(zhì)量蚌铜，測(cè)序樣本的測(cè)序次數(shù)锨侯，以及注釋的錯(cuò)誤）

10. large fold

change表達(dá)量不代表數(shù)據(jù)的明顯性（這些基因的isform多或這些基因測(cè)序測(cè)到的少，整體較低的表達(dá)）冬殃。cuffdiff中明顯表達(dá)倍數(shù)改變的基因囚痴，存在不確定性。

11.通過cufflinks產(chǎn)生的結(jié)果中transcript.gtf文件中cuff標(biāo)識(shí)的轉(zhuǎn)錄本就是新的轉(zhuǎn)錄本审葬。相應(yīng)的深滚，其他模塊輸出中CUFF標(biāo)識(shí)代表著新的轉(zhuǎn)錄本。

12. 若出現(xiàn)了如下錯(cuò)誤：

YouareusingCufflinksv2.2.1,whichisthemostrecentrelease.

open:Nosuchfileordirectory

File30doesn'tappeartobeavalidBAMfile,tryingSAM...

Error:cannotopenalignmentfile30forreading

這表明耳璧，你的參數(shù)有問題成箫。例如“--min-intron-length”,你設(shè)置為了：“-min-intron-length”

2. 使用方法

$ cufflinks [options]*

一個(gè)常用的例子：

$ cufflinks -p 8 -G transcript.gtf --library-type fr-unstranded -o cufflinks_output tophat_out/accepted_hits.bam

3. 普通參數(shù)

-h | --help

-o | --output-dir? default: ./設(shè)置輸出的文件夾名稱

-p | --num-threads? default: 1用于比對(duì)reads的CPU線程數(shù)

-G | --GTF 提供一個(gè)GFF文件展箱，以此來計(jì)算isoform的表達(dá)旨枯。此時(shí)，將不會(huì)組裝新的transcripts混驰，程序會(huì)忽略和reference transcript不兼容的比對(duì)結(jié)果

-g | --GTF-guide 提供GFF文件攀隔，以此來指導(dǎo)轉(zhuǎn)錄子組裝(RABT assembly)。此時(shí)栖榨，輸出結(jié)果會(huì)包含reference transcripts和novel genes and isforms昆汹。

-M | --mask-file 提供GFF文件。Cufflinks將忽略比對(duì)到該GTF文件的transcripts中的reads婴栽。該文件中常常是rRNA的注釋满粗，也可以包含線立體和其它希望忽略的transcripts的注釋。將這些不需要的RNA去除后愚争，對(duì)計(jì)算mRNA的表達(dá)量是有利的映皆。

-b | --frag-bias-correct 提供一個(gè)fasta文件來指導(dǎo)Cufflinks運(yùn)行新的bias detection and correction algorithm。這樣能明顯提高轉(zhuǎn)錄子豐度計(jì)算的精確性轰枝。

-u | --multi-read-correct讓Cufflinks來做initial estimation步驟捅彻，從而更精確衡量比對(duì)到genome多個(gè)位點(diǎn)的reads。

--library-type? default:fr-unstranded處理的reads具有鏈特異性鞍陨。比對(duì)結(jié)果中將會(huì)有個(gè)XS標(biāo)簽步淹。一般Illumina數(shù)據(jù)的library-type為 fr-unstranded。

--library-norm-method具體參考官網(wǎng),三種方式：classic-fpkm默認(rèn)的方式。geometric針對(duì)DESeq缭裆。quartile計(jì)算時(shí)键闺，fragments和總的map的count取75%

4. 豐度評(píng)估參數(shù)

-m | --frag-len-mean default: 200插入片段的平均長度。不過現(xiàn)在Cufflinks能learns插入片段的平均長度澈驼，因此不推薦自主設(shè)置此值艾杏。

-s | --frag-len-std-dev default: 80插入片段長度的標(biāo)準(zhǔn)差。不過現(xiàn)在Cufflinks能learns插入片段的平均長度盅藻，因此不推薦自主設(shè)置此值购桑。

-N | --upper-quartile-form使用75%分為數(shù)的值來代替總的值(比對(duì)到單一位點(diǎn)的fragments的數(shù)值)，作normalize氏淑。這樣有利于在低豐度基因和轉(zhuǎn)錄子中尋找差異基因勃蜘。

--total-hits-norm default: TRUECufflinks在計(jì)算FPKM時(shí),算入所有的fragments和比對(duì)上的reads。和下一個(gè)參數(shù)對(duì)立假残。默認(rèn)激活該參數(shù)缭贡。

--compatible-hits-norm Cufflinks在計(jì)算FPKM時(shí)，只針對(duì)和reference transcripts兼容的fragments以及比對(duì)上的reads辉懒。該參數(shù)默認(rèn)不激活阳惹，只能在有 --GTF 參數(shù)下有效，并且作 RABT或 ab initio 的時(shí)候無效眶俩。

--max-mle-iterations進(jìn)行極大似然法時(shí)選擇的迭代次數(shù)莹汤，默認(rèn)為：5000

--max-bundle-frags一個(gè)skipped locus/loci在別skipped前可以擁有的最大的fragment片段。默認(rèn)為1000000

--no-effective-length-correctionCufflinks will not employ its "effective" length normalization to transcript FPKM.Cufflinks將不會(huì)使用它的“effective” 長度標(biāo)準(zhǔn)化去計(jì)算轉(zhuǎn)錄的FPKM

--no-length-correctionCufflinks將根本不會(huì)使用轉(zhuǎn)錄本的長度去標(biāo)準(zhǔn)化fragment的數(shù)目颠印。當(dāng)fragment的數(shù)目和the features being quantified的size是獨(dú)立的纲岭，可以使用（例如for small RNA libraries, where no fragmentation takes place, or 3 prime end sequencing, where sampled RNA fragments are all essentially the same length).小心使用

5. 組裝常用參數(shù)

-L | --label? default: CUFFCufflink以GTF格式來報(bào)告轉(zhuǎn)錄子片段(transfrags),該參數(shù)是GTF文件的前綴

-F/--min-isoform-fraction <0.0-1.0>在計(jì)算一個(gè)基因的isoform 豐度后，過濾了豐度極低的轉(zhuǎn)錄本线罕，因?yàn)檫@些轉(zhuǎn)錄本不可以信任止潮。也可以過濾一些read匹配極低的外顯子。默認(rèn)為0.1或者10% of the most abundant isoform (the major isoform) of the gene.（一個(gè)基因的主要isoform的豐度的10%）

-j/--pre-mrna-fraction <0.0-1.0>內(nèi)含子被aligment覆蓋的最低深度钞楼。若小于這個(gè)值則那些內(nèi)含子的alignments被忽略掉喇闸。默認(rèn)為15%。 The minimum depth of coverage in the intronic region covered? ? ? by the alignment is divided by the number of spliced reads, and if the? ? ? ? ? result is lower than this parameter value, the intronic alignments are? ? ? ? ? ignored. The default is 15%.

-I/--max-intron-length內(nèi)含子的最大長度询件。若大于該值的內(nèi)含子燃乍，cufflinks不會(huì)報(bào)告。默認(rèn)為300000.Cufflinks will not report transcripts with? ? introns longer than this, and will ignore SAM alignments with REF_SKIP? ? ? ? ? CIGAR operations longer than this.? The default is 300,000.

-a/--junc-alpha <0.0-1.0>剪接比對(duì)過濾中假陽性的二項(xiàng)檢驗(yàn)中的 alpha value雳殊。默認(rèn)為 0.001

-A/--small-anchor-fraction <0.0-1.0>在junction中一個(gè)reads小于自身長度的這個(gè)百分比橘沥，會(huì)被懷疑，可能會(huì)在拼接前被過濾掉夯秃。默認(rèn)為0.09

--min-frags-per-transfrag? default: 10組裝出的transfrags被支持的RNA-seq的fragments數(shù)少于該值則不被報(bào)道座咆。

--overhang-tolerance當(dāng)決定一個(gè)reads或轉(zhuǎn)錄本與某個(gè)轉(zhuǎn)錄本兼容或匹配的時(shí)候痢艺，允許的能加入該轉(zhuǎn)錄本的外顯子的延伸長度。默認(rèn)是8bp和bowtie/tophat默認(rèn)的一致介陶。

--max-bundle-lengthMaximum genomic length allowed for a given bundle.? The default is 3,500,000bp.

--min-intron-length? default: 50最小的intron大小堤舒。

--trim-3-avgcov-thresh最小的3‘端的平均覆蓋程度。小于該值哺呜，則刪除其3’端序列舌缤。默認(rèn)10Minimum average coverage required to attempt 3' trimming.? The default is 10.

--trim-3-dropoff-frac最低百分比的拼接的轉(zhuǎn)錄本的3‘端的平均覆蓋程度。默認(rèn)0.1The fraction of average coverage below which to trim the 3' end of an assembled? ? ? ? ? transcript.? The default is 0.1.

--max-multiread-fraction <0.0-1.0>若一個(gè)轉(zhuǎn)錄本Transfrags的reads能匹配到基因組的多個(gè)位置某残，其中該轉(zhuǎn)錄本的reads有超過該百分比是multireads国撵，則不會(huì)報(bào)告這個(gè)轉(zhuǎn)錄本。默認(rèn)為75%The fraction a transfrag's supporting reads that may be multiply mapped to the genome. A transcript composed of more than this fraction will not be reported by the assembler.? Default: 0.75 (75% multireads or more is suppressed).

--overlap-radius? default: 50Transfrags之間的距離少于該值玻墅，則將其連到一起介牙。

Advanced Reference Annotation Based Transcript (RABT) Assembly Options:當(dāng)你使用-g/--GTF-guide這個(gè)參數(shù)時(shí)，需要考慮的選項(xiàng)澳厢。

--3-overhang-tolerance當(dāng)決定一個(gè)拼接的轉(zhuǎn)錄本（這個(gè)轉(zhuǎn)錄本可能不是新的轉(zhuǎn)錄本）和一個(gè)參考轉(zhuǎn)錄本是否合并時(shí)环础，參考轉(zhuǎn)錄本的3‘端允許延伸的長度。默認(rèn)600bpThe number of bp allowed to overhang the 3' end of a reference transcript when determining? ? ? if an assembled transcript should be merged with it (ie, the assembled transcript is not novel).? ? ? ? The default is 600 bp.

--intron-overhang-tolerance當(dāng)決定一個(gè)拼接的轉(zhuǎn)錄本（這個(gè)轉(zhuǎn)錄本可能不是新的轉(zhuǎn)錄本）和一個(gè)參考轉(zhuǎn)錄本是否合并時(shí)剩拢，參考轉(zhuǎn)錄本的外顯子允許延伸的長度线得。默認(rèn)50bpThe number of bp allowed to enter the intron of a reference transcript when determining if an? ? assembled transcript should be merged with it (ie, the assembled transcript is not novel).? ? ? The default is 50 bp.

--no-faux-readsThis option disables tiling of the reference transcripts with faux reads.? Use this if you only? ? ? ? want to use sequencing reads in assembly but do not want to output assembled transcripts that lay? ? ? within reference transcripts.? All reference transcripts in the input annotation will also? ? ? be included in the output.這一項(xiàng)將不能掩蓋參考轉(zhuǎn)錄組中的假reads。當(dāng)你只想在拼接中使用測(cè)序的reads而不想輸出lay within reference transcripts的拼接的轉(zhuǎn)錄組徐伐。輸入時(shí)注釋的所有的參考轉(zhuǎn)錄組也將會(huì)輸入到輸出中贯钩。

其他參數(shù)（無關(guān)緊要）

-v/--verbose顯示版本信息等等

-q/--quiet除了警告和錯(cuò)誤外，其他信息將不會(huì)print

--no-update-check關(guān)系cufflinks自動(dòng)更新的能力

6. Cufflinks輸出結(jié)果

cufflinks的輸入文件是sam或bam格式呵晨。并且sam或bam格式的文件必須排好序魏保。（The SAM file supplied to Cufflinksmustbe sorted by? ? ? ? ? reference position.）Tophat的輸出結(jié)果sam或bam已經(jīng)排好了序熬尺。針對(duì)其他的未排序的sam或bam文件采用如下排序方式：

sort -k 3,3 -k 4,4n hits.sam > hits.sam.sorted

1. transcripts.gtf

該文件包含Cufflinks的組裝結(jié)果isoforms摸屠。前7列為標(biāo)準(zhǔn)的GTF格式，最后一列為attributes粱哼。其每一列的意義：

列數(shù)? 列的名稱? 例子? ? ? ? 描述

1? ? 序列名? ? chrX? ? ? ? 染色體或contig名; 2? ? 來源? ? ? Cufflinks? 產(chǎn)生該文件的程序名; 3? ? 類型? ? ? exon? ? ? ? 記錄的類型季二，一般是transcript或exon; 4? ? 起始? ? ? 1? ? ? ? ? 1-base的值; 5? ? 結(jié)束? ? ? 1000? ? ? ? 結(jié)束位置; 6? ? 得分? ? ? 1000? ? ? ? ; 7? ? 鏈? ? ? ? +? ? ? ? ? Cufflinks猜測(cè)isoform來自參考序列的那一條鏈，一般是'+','-'或'.';8? ? frame? ? .? ? ? ? ? Cufflinks不去預(yù)測(cè)起始或終止密碼子框的位置; 9? ? attributes? ...? ? ? 詳見下

每一個(gè)GTF記錄包含如下attributes：

Attribute? ? ? 例子? ? ? 描述

gene_idCUFF.1Cufflinks的gene id;transcript_idCUFF.1.1? Cufflinks的轉(zhuǎn)錄子 id; FPKM? ? ? ? ? 101.267? isoform水平上的豐度,FragmentsPerKilobase of exon model perMillion mapped fragments; frac? ? ? ? ? 0.7647? ? 保留著的一項(xiàng)揭措，忽略即可胯舷，以后可能會(huì)取消這個(gè);conf_lo? ? ? ? 0.07? ? ? isoform豐度的95%置信區(qū)間的下邊界，即 下邊界值 = FPKM * ( 1.0 - conf_lo );conf_hi? ? ? ? 0.1102? ? isoform豐度的95%置信區(qū)間的上邊界绊含，即 上邊界值 = FPKM * ( 1.0 + conf_hi ); cov? ? ? ? ? ? 100.765? 計(jì)算整個(gè)transcript上read的覆蓋度;full_read_support? yes? 當(dāng)使用 RABT assembly 時(shí)桑嘶，該選項(xiàng)報(bào)告所有的introns和exons是否完全被reads所覆蓋

2. ispforms.fpkm_tracking

isoforms(可以理解為gene的各個(gè)外顯子)的fpkm計(jì)算結(jié)果

3. genes.fpkm_tracking

gene的fpkm計(jì)算結(jié)果

四.Cuffmerge的使用

1. Cuffmerge簡介

Cuffmerge將各個(gè)Cufflinks生成的transcripts.gtf文件融合稱為一個(gè)更加全面的transcripts注釋結(jié)果文件merged.gtf。以利于用Cuffdiff來分析基因差異表達(dá)躬充。

2. 使用方法

$ cuffmerge [options]*

輸入文件為一個(gè)文本文件逃顶，是包含著GTF文件路徑的list讨便。常用例子：

$ cuffmerge -o ./merged_asm -p 8 assembly_list.txt

3. 使用參數(shù)

-h | --help

-o? default: ./merged_asm

將結(jié)果輸出至該文件夾。

-g | --ref-gtf將該reference GTF一起融合到最終結(jié)果中以政。

-p | --num-threads? defautl: 1

使用的CPU線程數(shù)

-s | --ref-sequence /該參數(shù)指向基因組DNA序列霸褒。如果是一個(gè)文件夾，則每個(gè)contig則是一個(gè)fasta文件盈蛮；如果是一個(gè)fasta文件废菱，則所有的contigs都需要在里面。Cuffmerge將使用該ref-sequence來幫助對(duì)transfrags分類抖誉，并排除repeats殊轴。比如transcripts包含一些小寫堿基的將歸類到repeats.

4. Cuffmerge輸出結(jié)果

輸出的結(jié)果文件默認(rèn)為 /merged.gtf

五.Cuffcompare的使用

1. Cuffcompare簡介

Cuffcompare使用Cufflinks的GTF結(jié)果，對(duì)GTF結(jié)果進(jìn)行比較袒炉。和reference gtf比較尋找novel轉(zhuǎn)錄本等梳凛。

2. Cuffcompare的使用方法

$ cuffcompare [options]*? [cuff2.gtf] ... [cuffN.gtf]

使用例子：

$ cuffcompare -o cuffcmp cuff1.gtf cuff2.gtf

3. 使用參數(shù)

-h-V顯示進(jìn)程

-C默認(rèn)，表示"contained" transcripts 也會(huì)寫入.combined.gtf中梳杏。

-o? default: cuffcmp輸出文件的前綴

-r 參考的GFF文件韧拒。用來評(píng)估輸入的gtf文件中g(shù)ene models的精確性。每一個(gè)輸入的gtf的isoforms將和該參考文件進(jìn)行比較十性，并被標(biāo)注為 overlapping, matching 或 novel叛溢。

-R當(dāng)有了 -r 參數(shù)時(shí)，指定該參數(shù)時(shí)劲适，將忽略參考GFF文件中的一些transcripts楷掉。這些transcripts不和任何輸入的GTF文件overlapped。

-s

該參數(shù)指向基因組DNA序列霞势。如果是一個(gè)文件夾烹植，則每個(gè)contig則是一個(gè)fasta文件；如果是一個(gè)fasta文件愕贡，則所有的contigs都需要在里面草雕。小寫字母的堿基用來將相應(yīng)的transcripts作為repeats處理。

4.輸出結(jié)果

在當(dāng)前目錄下輸出3個(gè)文件：

.stats固以， 報(bào)告與參考注釋比較時(shí)墩虹，各種與準(zhǔn)確性相關(guān)的數(shù)據(jù)。其中憨琳，Sn和Sp展示的是specificity and sensitivity values诫钓。fSnandfSp列展示的 "fuzzy" variants of these same accuracy calculations。允許存在變動(dòng)篙螟。（-o 沒有設(shè)置菌湃，默認(rèn)為cuffcmp為文件前綴）

.combined.gtf報(bào)告每個(gè)樣本的所有的 transfrags 的信息。若一個(gè)transfrag在多個(gè)樣本中遍略，它只報(bào)道一次惧所。

.tracking匹配到樣本間的轉(zhuǎn)錄本场梆。this file matches transcripts up between samples.? Each row contains? ? ? ? ? ? ? ? a transcript structure that is present in one or more input GTF files.? ? ? ? ? ? ? ? Because the transcripts will generally have different IDs (unless you? ? ? ? ? ? ? ? assembled your RNA-Seq reads against a reference transcriptome),cuffcompareexamines the structure of each the transcripts,? ? ? ? ? ? ? ? matching transcripts that agree on the coordinates and order of all of? ? ? ? ? ? ? ? their introns, as well as strand.? Matching transcripts are allowed to? ? ? ? ? ? ? ? differ on the length of the first and last exons, since these lengths? ? ? ? ? ? ? ? will naturally vary from sample to sample due to the random nature of? ? ? ? ? ? ? ? sequencing.

例子；

TCONS_00000045 XLOC_000023 Tcea|uc007afj.1? ? j? ? ? \? ? q1:exp.115|exp.115.0|100|3.061355|0.350242|0.350207 \? ? q2:60hr.292|60hr.292.0|100|4.094084|0.000000|0.000000

In this example, a transcript present in the two input files,calledexp.115.0in the first and60hr.292.0inthe second, doesn't match any reference transcript exactly, butshares exons withuc007afj.1, an isoform of the gene Tcea,as indicated by theclass

codej. The first three columns are as follows:

其中纯路，1 Cufflinks transfrag idTCONS_00000045內(nèi)部的transfrag id或油；2Cufflinks locus idXLOC_000023內(nèi)部的locus id； 3Reference gene idTcea參考的注釋的gene的id或者“-”表示沒有匹配到參考的轉(zhuǎn)錄本驰唬； 4Reference transcript iduc007afj.1參考的注釋的轉(zhuǎn)錄本的id或者“-”表示沒有匹配到參考的轉(zhuǎn)錄本顶岸； 5 Class codec轉(zhuǎn)錄本和參考轉(zhuǎn)錄本之間的匹配類型。第五列之后如下：

qJ: | | | | | | |

在輸入的GTF的同目錄下輸出.refmap 和 .tmap 文件叫编。

.refmap具體內(nèi)容如下：

1Reference gene name參考注釋的gtf中的基因名字 2 Reference transcript id 參考的轉(zhuǎn)錄本id3Class code 表示cufflinks拼接的轉(zhuǎn)錄本和參考轉(zhuǎn)錄本間的匹配情況：c 表示部分匹配辖佣；= 表示全部匹配

4Cufflinks matches匹配到參考轉(zhuǎn)錄本的cufflinks拼接的轉(zhuǎn)錄本的id

.tmap具體內(nèi)容如下：

1Reference gene name參考注釋的gtf中的基因名字 2 Reference transcript id 參考的轉(zhuǎn)錄本id3Class code 表示cufflinks拼接的轉(zhuǎn)錄本和參考轉(zhuǎn)錄本間的匹配情況：c 表示部分匹配；= 表示全部匹配

4 Cufflinks gene id; 5 Cufflinks transcript id;6 Fraction of major isofor m (FMI) ; 7FPKM ; 8 FPKM_conf_lo; 9FPKM_conf_hi; 10 Coverage ; 11 Length; 12Major isoform ID

class cord :

PriorityCodeDescription

1=Complete match of intron chain

2cContained

3jPotentially novel isoform (fragment): at least one

splice junction is shared with a reference transcript

4eSingle exon transfrag overlapping a reference exon

and at least 10 bp of a reference intron, indicating a possible

pre-mRNA fragment.

5iA transfrag falling entirely within a reference

intron

6oGeneric exonic overlap with a reference

transcript

7pPossible polymerase run-on fragment (within

2Kbases of a reference transcript)

8rRepeat. Currently determined by looking at the

soft-masked reference sequence and applied to transcripts where at

least 50% of the bases are lower case

9uUnknown, intergenic transcript

10xExonic overlap with reference on the opposite

strand

11sAn 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)

六.Cuffdiff的使用

1. Cuffdiff簡介

用于尋找轉(zhuǎn)錄子表達(dá)的顯著性差異搓逾。

2. Cuffdiff使用方法

cuffdiff主要是發(fā)現(xiàn)轉(zhuǎn)錄本表達(dá)卷谈，剪接，啟動(dòng)子使用的明顯變化霞篡。

cuffdiff [options]* ...

[sampleN.sam_replicate1.sam[,...,sample2_replicateM.sam]]

$ cuffdiff [options]*? ...[sampleN_1.sam[,...,sampleN_M.sam]]其中transcripts.gtf是由cufflinks世蔗，cuffcompare，cuffmerge所生成的文件朗兵，或是由其它程序生成的污淋。一個(gè)樣本有多個(gè)replicate，用逗號(hào)隔開余掖。sample多于一個(gè)時(shí)寸爆，cuffdiff將比較samples間的基因表達(dá)的差異性。一個(gè)常用例子：$ cuffdiff --lables lable1,lable2 -p 8 --time-series --multi-read-correct --library-type fr-unstranded --poisson-dispersion transcripts.gtf sample1.sam sample2.sam

cuffdiff接受bam/sam或cuffquant的CXB文件盐欺，同時(shí)也可以接受bam與sam的混合文件赁豆，不能接受bam/sam和CXB的混合文件。

3. 使用參數(shù)

-h | --help

-o | --output-dir? default: ./

輸出的文件夾目錄冗美。

-L | --lables? default: q1,q2,...qN

給每個(gè)sample一個(gè)樣品名或者一個(gè)環(huán)境條件一個(gè)lable

-p | --num-threads? default: 1

使用的CPU線程數(shù)

-T | --time-series

讓Cuffdiff來按樣品順序來比對(duì)樣品魔种，而不是對(duì)所有的samples都進(jìn)行兩兩比對(duì)。即第二個(gè)SAM和第一個(gè)SAM比墩衙；第三個(gè)SAM和第二個(gè)SAM比务嫡；第四個(gè)SAM和第三個(gè)SAM比...

-N | --upper-quartile-form

使用75%分為數(shù)的值來代替總的值(比對(duì)到單一位點(diǎn)的fragments的數(shù)值)，作normalize漆改。這樣有利于在低豐度基因和轉(zhuǎn)錄子中尋找差異基因。

--total-hits-norm

Cufflinks在計(jì)算FPKM時(shí),算入所有的fragments和比對(duì)上的reads准谚。和下一個(gè)參數(shù)對(duì)立挫剑。默認(rèn)不激活該參數(shù)。

--compatible-hits-normCufflinks在計(jì)算FPKM時(shí)柱衔，只針對(duì)和reference transcripts兼容的fragments以及比對(duì)上的reads樊破。該參數(shù)默認(rèn)激活愉棱，使用該參數(shù)可以降低核糖體rna的reads對(duì)基因表達(dá)的干擾。

-b | --frag-bias-correct（一般是genome.fa）提供一個(gè)fasta文件來指導(dǎo)Cufflinks運(yùn)行新的bias detection and correction algorithm哲戚。這樣能明顯提高轉(zhuǎn)錄子豐度計(jì)算的精確性奔滑。

-u | --multi-read-correct讓Cufflinks來做initial estimation步驟，從而更精確衡量比對(duì)到genome多個(gè)位點(diǎn)的reads顺少。

-c | --min-alignment-count? default: 10

如果比對(duì)到某一個(gè)位點(diǎn)的fragments數(shù)目少于該值朋其，則不做該位點(diǎn)的顯著性分析。認(rèn)為該位點(diǎn)的表達(dá)量沒有顯著性差異脆炎。

-M | --mask-file

提供GFF文件梅猿。Cufflinks將忽略比對(duì)到該GTF文件的transcripts中的reads。該文件中常常是rRNA的注釋秒裕，也可以包含線立體和其它希望忽略的transcripts的注釋袱蚓。將這些不需要的RNA去除后，對(duì)計(jì)算mRNA的表達(dá)量是有利的几蜻。

-FDR? default: 0.05允許的false discovery rate.

--library-type default:fr-unstranded處理的reads具有鏈特異性喇潘。比對(duì)結(jié)果中將會(huì)有個(gè)XS標(biāo)簽。一般Illumina數(shù)據(jù)的library-type為 fr-unstranded梭稚。

--dispersion-method

其他高級(jí)參數(shù)：

-m | --frag-len-mean default: 200插入片段的平均長度土全。不過現(xiàn)在Cufflinks能learns插入片段的平均長度，因此不推薦自主設(shè)置此值撬腾。

-s | --frag-len-std-dev default: 80

插入片段長度的標(biāo)準(zhǔn)差椅亚。不過現(xiàn)在Cufflinks能learns插入片段的平均長度，因此不推薦自主設(shè)置此值扼褪。-v/--verbose顯示版本信息等等

-q/--quiet除了警告和錯(cuò)誤外想幻，其他信息將不會(huì)print

--no-update-check關(guān)系cufflinks自動(dòng)更新的能力

-F/--min-isoform-fraction <0.0-1.0>建議不要更改，主要的isorform豐度若低于這個(gè)分?jǐn)?shù)话浇，可變的isoform將四舍五入為0.默認(rèn)為1e-5

--max-bundle-frags一個(gè)skipped locus/loci在skipped前可以擁有的最大的fragment片段脏毯。默認(rèn)為1000000

--max-frag-count-draws （默認(rèn)為100）和--max-frag-assign-draws （默認(rèn)為50）--min-reps-for-js-test一個(gè)針對(duì)不同調(diào)控的基因做test的最小的復(fù)制次數(shù)。Cuffdiff won't test genes for differential regulation unless the

conditions in question have at least this many replicates.? Default: 3.

--no-effective-length-correctionCuffdiff will not employ its "effective" length normalization to transcript FPKM. Cufflinks將不會(huì)使用它的“effective” 長度標(biāo)準(zhǔn)化去計(jì)算轉(zhuǎn)錄的FPKM

--no-length-correctioncufflinks將根本不會(huì)使用轉(zhuǎn)錄本的長度去標(biāo)準(zhǔn)化fragment的數(shù)目幔崖。當(dāng)fragment的數(shù)目和the

features being quantified的size是獨(dú)立的食店，可以使用（例如for small RNA libraries,

where no fragmentation takes place, or 3 prime end sequencing, where

sampled RNA fragments are all essentially the same length).小心使用

--max-mle-iterations極大似然法的迭代次數(shù)，默認(rèn)5000

--poisson-dispersionUse the Poisson fragment dispersion model instead of learning one in each condition.

4.Cuffdiff輸出

1. FPKM tracking filescuffdiff計(jì)算每個(gè)樣本中的轉(zhuǎn)錄本赏寇，初始轉(zhuǎn)錄本和基因的FPKM吉嫩。其中，基因和初始轉(zhuǎn)錄本的FPKM的計(jì)算是在每個(gè)轉(zhuǎn)錄本group和基因group中的轉(zhuǎn)錄本的FPKM的求和嗅定。

isoforms.fpkm_trackingTranscript FPKMs

genes.fpkm_trackingGene FPKMs. Tracks the summed FPKM of transcriptssharing eachgene_id

cds.fpkm_trackingCoding sequence FPKMs. Tracks the summed FPKM oftranscripts sharing eachp_id, independent oftss_id

tss_groups.fpkm_trackingPrimary transcript FPKMs. Tracks the summed FPKMof transcripts sharing eachtss_id

2. Count tracking files評(píng)估每個(gè)樣本中來自每個(gè) transcript, primary transcript,? ? ? ? ? ? ? ? and gene的fragment數(shù)目自娩。其中primary transcript,? ? ? ? ? ? ? ? and gene的fragment數(shù)目是每個(gè)primary transcript group或gene group中trancript的數(shù)目之和。

isoforms.count_trackingTranscript counts

genes.count_trackingGene counts. Tracks the summed counts oftranscripts sharing eachgene_id

cds.count_trackingCoding sequence counts. Tracks the summed countsof transcripts sharing eachp_id, independent oftss_id

tss_groups.count_trackingPrimary transcript counts. Tracks the summedcounts of transcripts sharing eachtss_id

3. Read group tracking files計(jì)算在每個(gè)repulate中每個(gè)transcript渠退， primary transcript和gene的表達(dá)量和frage數(shù)目

isoforms.read_group_trackingTranscript read group tracking

genes.read_group_trackingGene read group tracking. Tracks the summedexpression and counts of transcripts sharing eachgene_idin each replicate

cds.read_group_trackingCoding sequence FPKMs. Tracks the summedexpression and counts of transcripts sharing eachp_id,independent oftss_idin each replicate

tss_groups.read_group_trackingPrimary transcript FPKMs. Tracks the summedexpression and counts of transcripts sharing eachtss_idin each replicate

4. Differential expression test對(duì)于splicing transcript忙迁，? ? ? ? ? ? ? ? primary transcripts, genes, and coding sequences.樣本之間的表達(dá)差異檢驗(yàn)脐彩。對(duì)于每一對(duì)樣本x和y，都會(huì)有以下四個(gè)文件：

isoform_exp.diffTranscript differential FPKM.

gene_exp.diffGene differential FPKM. Tests difference sin thesummed FPKM of transcripts sharing eachgene_id

tss_group_exp.diffPrimary transcript differential FPKM. Testsdifferences in the summed FPKM of transcripts sharing eachtss_id

cds_exp.diffCoding sequence differential FPKM. Testsdifferences in the summed FPKM of transcripts sharing eachp_idindependent oftss_id

每個(gè)文件的樣式如下：

Column numberColumn nameExampleDescription

1Tested idXLOC_000001A unique identifier describing the transcipt,

gene, primary transcript, or CDS being tested

2geneLypla1Thegene_name(s) orgene_id(s)being tested

3locuschr1:4797771-4835363Genomic coordinates for easy browsing to the genes

or transcripts being tested.

4sample 1LiverLabel (or number if no labels provided) of the

first sample being tested

5sample 2BrainLabel (or number if no labels provided) of the

second sample being tested

6Test statusNOTESTCan be one of OK (test successful), NOTEST (not

enough alignments for testing), LOWDATA (too complex or shallowly

sequenced), HIDATA (too many fragments in locus), or FAIL, when an

ill-conditioned covariance matrix or other numerical exception

prevents testing.

7FPKMx8.01089FPKM of the gene in samplex

8FPKMy8.551545FPKM of the gene in sampley

9log2(FPKMy/FPKMx)0.06531The (base 2) log of the fold changey/x

10test stat0.860902The value of the test statistic used to compute

significance of the observed change in FPKM

11p value0.389292Theuncorrectedp-value of thetest statistic

12q value0.985216TheFDR-adjustedp-value of thetest statistic

13significantnoCan be either "yes" or "no", depending on whetherpis greater then the FDRafterBenjamini-Hochbergcorrection for multiple-testing

5. Differential splicing tests – splicing.diff對(duì)于每個(gè)primary transcript姊扔，鑒定的不同的isoform的差異性惠奸。只有2個(gè)或2個(gè)以上的isoforms的primary transcript存在

Column numberColumn nameExampleDescription

1Tested idTSS10015A unique identifier describing the primary

transcript being tested.

2gene nameRtknThegene_nameorgene_idthatthe primary transcript being tested belongs to

3locuschr6:83087311-83102572Genomic coordinates for easy browsing to the genes

or transcripts being tested.

4sample 1LiverLabel (or number if no labels provided) of the

first sample being tested

5sample 2BrainLabel (or number if no labels provided) of the

second sample being tested

6Test statusOKCan be one of OK (test successful), NOTEST (not

enough alignments for testing), LOWDATA (too complex or shallowly

sequenced), HIDATA (too many fragments in locus), or FAIL, when an

ill-conditioned covariance matrix or other numerical exception

prevents testing.

7Reserved0

8Reserved0

9√JS(x,y)0.22115The splice overloading of the primary transcript,

as measured by the square root of the Jensen-Shannon divergence

computed on the relative abundances of the splice variants

10test stat0.22115The value of the test statistic used to compute

significance of the observed overloading, equal to √JS(x,y)

11p value0.000174982Theuncorrectedp-value of thetest statistic.

12q value0.985216TheFDR-adjustedp-value of thetest statistic

13significantyesCan be either "yes" or "no", depending on whetherpis greater then the FDRafterBenjamini-Hochbergcorrection for multiple-testing

6. Differential coding output – cds.diff對(duì)于每個(gè)基因，它的cds的鑒定恰梢。樣本間的輸出cds的差異性佛南。只有2個(gè)或2個(gè)以上的cds（multi-protein genes）列舉在文件中。

Column numberColumn nameExampleDescription

1Tested idXLOC_000002-[chr1:5073200-5152501]A unique identifier describing the gene being

tested.

2gene nameAtp6v1hThegene_nameorgene_id

3locuschr1:5073200-5152501Genomic coordinates for easy browsing to the genes

or transcripts being tested.

4sample 1LiverLabel (or number if no labels provided) of the

first sample being tested

5sample 2BrainLabel (or number if no labels provided) of the

second sample being tested

6Test statusOKCan be one of OK (test successful), NOTEST (not

enough alignments for testing), LOWDATA (too complex or shallowly

sequenced), HIDATA (too many fragments in locus), or FAIL, when an

ill-conditioned covariance matrix or other numerical exception

prevents testing.

7Reserved0

8Reserved0

9√JS(x,y)0.0686517The CDS overloading of the gene, as measured by

the square root of the Jensen-Shannon divergence computed on the

relative abundances of the coding sequences

10test stat0.0686517The value of the test statistic used to compute

significance of the observed overloading, equal to √JS(x,y)

11p value0.00546783Theuncorrectedp-value of thetest statistic

12q value0.985216TheFDR-adjustedp-value of thetest statistic

13significantyesCan be either "yes" or "no", depending on whetherpis greater then the FDRafterBenjamini-Hochbergcorrection for multiple-testing

7. Differential promoter use – promoters.diff樣本間啟動(dòng)子使用的差異性删豺。只有表達(dá)2個(gè)或2個(gè)以上isoform的基因列舉在這里共虑。

8. Read group info – read_groups.info每個(gè)repulate，在進(jìn)行定量分析時(shí)呀页，cuffdiff的關(guān)鍵屬性會(huì)列出妈拌。

Column numberColumn nameExampleDescription

1filemCherry_rep_A/accepted_hits.bamBAM or SAM file containing the data for the read

group

2conditionmCherryCondition to which the read group belongs

3replicate_num0Replicate number of the read group

4total_mass4.72517e+06Total number of fragments for the read group

5norm_mass4.72517e+06Fragment normalization constant used during

calculation of FPKMs.

6internal_scale1.23916Internal scaling factor, used to transform

replicates of a single condition onto the "internal" common count

scale.

7external_scale0.96External scaling factor, used to transform counts

from different conditions onto an internal common count scale.

9. Run info – run.info運(yùn)行的信息。

其中：輸出文件FPKM Tracking file的格式如下：

1tracking_idTCONS_00000001內(nèi)部唯一object的id（識(shí)別基因蓬蝶，轉(zhuǎn)錄本尘分，CDS，初始轉(zhuǎn)錄本）A

unique identifier describing the object (gene, transcript, CDS,

primary transcript)

2class_code=內(nèi)部定義的類別的id丸氛，“-”表明不是轉(zhuǎn)錄本培愁。Theclass_codeattribute for the object, or "-" if not a transcript, or ifclass_codeisn't present

3nearest_ref_idNM_008866.1最接近的參考轉(zhuǎn)錄本The

reference transcript to which the class code refers, if

any

4gene_idNM_008866基因idThegene_id(s)

associated with the object

5gene_short_nameLypla1基因名字Thegene_short_name(s)

associated with the object

6tss_idTSS1初始轉(zhuǎn)錄本id，或者“-”表示沒有初始轉(zhuǎn)錄本缓窜。Thetss_idassociated with the object, or "-" if not a transcript/primary

transcript, or iftss_idisn't

present

7locuschr1:4797771-4835363基因組上的位置Genomic

coordinates for easy browsing to the object

8length2447轉(zhuǎn)錄本的長度The

number of base pairs in the transcript, or '-' if not a

transcript/primary transcript

9coverage43.4279read覆蓋深度的估測(cè)值Estimate for the absolute depth of read coverage across the

object

10q0_FPKM8.01089樣本0中object的FPKMFPKMof

the object in sample 0

11q0_FPKM_lo7.03583object在樣本0中FPKM的95%置信區(qū)間的下界the

lower bound of the 95% confidence interval on the FPKM of the

object in sample 0

12q0_FPKM_hi8.98595object在樣本0中FPKM的95%置信區(qū)間的上界the

upper bound of the 95% confidence interval on the FPKM of the

object in sample 0

13q0_statusOKobject在樣本0中的量化狀態(tài)定续，0K表示成功，LOWDATA:太復(fù)雜或測(cè)序深度不夠禾锤；HIDATA：在一個(gè)基因座上太多fragments私股，F(xiàn)AIL：失敗的協(xié)方差矩陣或其他數(shù)值阻止了去卷積Quantification

status for the object in sample 0. Can be one of OK

(deconvolutionsuccessful), LOWDATA (too complex or shallowly sequenced), HIDATA

(too many fragments in locus), or FAIL, when an ill-conditioned

covariance matrix or other numerical exception preventsdeconvolution.

Count tracking files 格式如下:

1tracking_idTCONS_00000001A unique identifierdescribing the object (gene, transcript, CDS, primarytranscript)

2q0_count201.334Estimated (externally scaled) number of fragments generated by theobject in sample 0

3q0_count_variance5988.24Estimated variance inthe number of fragments generated by the object in sample0

4q0_count_uncertainty_var170.21Estimated variance inthe number of fragments generated by the object in sample 0 due tofragment assignment uncertainty.

5q0_count_dispersion_var4905.63Estimated variance inthe number of fragments generated by the object in sample 0 due tocross-replicate variability.

6q0_statusOKQuantification status for the object in sample 0. Can be one of OK(deconvolutionsuccessful), LOWDATA (too complex or shallowly sequenced), HIDATA

(too many fragments in locus), or FAIL, when an ill-conditioned

covariance matrix or other numerical exception preventsdeconvolution.

七. cufflinks使用中遇到的問題

1.

使用cuffdiff時(shí)候，在最新版本下恩掷，無重復(fù)的RNA-seq樣作比較倡鲸，結(jié)果中沒有差異表達(dá)基因？

在v2.0.1及之后的版本中cuffdiff貌似不支持無重復(fù)的RNA-seq數(shù)據(jù)了黄娘。使用之前的版本即可峭状。

八 Cuffquant

cuffquant是cuffquant能夠?qū)蝹€(gè) BAM文件的基因轉(zhuǎn)錄本表達(dá)水平進(jìn)行定量分析。生成的是CXB文件abundances.cxb,逼争，可以作為cuffdiff的輸入优床，這會(huì)加快cuffdiff的運(yùn)行速度。也可以作為Cuffnorm的輸入氮凝。

具體使用：Usage: cuffquant [options]*

它的參數(shù)：(和前面參數(shù)的含義是一樣的)

-h/--help羔巢；-o/--output-dir

；-p/--num-threads 罩阵；-M/--mask-file

竿秆；-b/--frag-bias-correct

；-u/--multi-read-correct稿壁；--library-type幽钢；-m/--frag-len-mean

；-s/--frag-len-std-dev 傅是；--max-mle-iterations

匪燕；--max-bundle-frags

；--no-effective-length-correction喧笔；--no-length-correction帽驯；-v/--verbose；-q/--quiet书闸；--no-update-check尼变；

九Cuffnorm

cuffnorm能夠用 cuffquant

的輸出文件作為輸入文件，對(duì)基因和轉(zhuǎn)錄組浆劲，簡單計(jì)算標(biāo)準(zhǔn)化過的表達(dá)水平嫌术。當(dāng)你想要的是一系列可比較的基因、轉(zhuǎn)錄組牌借、CDS 組和 TSS

組的表達(dá)值時(shí)度气，可是使用 cuffnorm。例如膨报，當(dāng)你僅僅想對(duì)單個(gè)基因的表達(dá)值做個(gè)熱圖或者點(diǎn)圖時(shí)磷籍。

cuffnorm [options]* ...[sampleN.sam_replicate1.sam[,...,sample2_replicateM.sam]]

具體參數(shù)：它的參數(shù)和前面的類似，可以看前面的相關(guān)參數(shù)现柠。

-h/--help院领；-o/--output-dir

；-L/--labels 晒旅；-p/--num-threads 栅盲；

--total-hits-norm（默認(rèn)不激活）；--compatible-hits-norm（默認(rèn)激活）废恋；--library-type谈秫；--library-norm-method；--output-format鱼鼓；-v/--verbose拟烫；-q/--quiet；--no-update-check迄本；

cuffnorm的輸出文件是實(shí)驗(yàn)中的each gene, transcript, TSS group, andCDSgroup的標(biāo)準(zhǔn)化的表達(dá)水平硕淑。不做表達(dá)差異的分析。cuffnorm的輸出文件默認(rèn)是“simple-table”的文件。這些文件和cuffdiff輸出的文件格式不同置媳。若你想要cuffdiff格式的文件于樟，你需要輸入命令：--output-format cuffdiff

cuffnorm報(bào)道FPKM values andnormalized,estimates for the number of fragments that originate from eachgene, transcript, TSS group, and CDSgroup.這些結(jié)果已經(jīng)做了標(biāo)準(zhǔn)化處理。對(duì)于某些下游軟件需要原始文件拇囊，是不作為其輸入的迂曲。

可以創(chuàng)建一個(gè)文件，例如sample_sheet.txt作為cuffdiff或cuffnorm的輸入（存入sam文件的path）寥袭。文件格式如下：

sample_idgroup_label

C1_R1.samC1

C1_R2.samC1

C2_R1.samC2

C2_R2.samC2

輸出結(jié)果文件如下：

FPKM tracking files：估測(cè)的基因的表達(dá)水平

Count tracking files：估測(cè)的基因的fragment count values

Read group tracking files：報(bào)道per-replicate expression and count

data.

對(duì)于每個(gè)genes, transcripts, TSS groups, and CDS

groups路捧，cuffnorm會(huì)報(bào)道兩種文件形式： *.fpkm_table files and *.count_table

files。