User Guide. Honglei Liu.

Transcription

1 User Guide Hnglei Liu Overview This package includes the implementatin f the anchr based clustering algrithm (ASC) and an integrated fast mtif discvery tl ASC+MEME. ASC+MEME prvides similar interfaces and utputs as MEME but is five rders f magnitude faster. Usage agreement Dwnlading is fr internal research purpse nly. Redistributin and cmmercial usage are nt permitted. Fr ther interests, cntact the authrs (liuhnglei@gmail.cm). Prerequisite T use ASC+MEME, yu need t have MEME Suite installed. The executable files f MEME Suite shuld be added t the PATH envirnment variable, r the fllwing executable files shuld be cpied t the the installatin directry f ASC+MEME: meme meme2images fim Yu will als need t have Pythn 2.7 installed. Installatin: Yu can dwnlad and decmpress the package by running wget tar -xvzf ASC_MEME.tar.gz cd ASC_MEME

2 This package includes the executable file fr ASC that culd run in a Linux envirnment. If yu need the surce cde, please cntact us. Nte: after the package is dwnladed and decmpressed, remember t cpy the MEME executables t the package directry. Step by Step Input file ASC+MEME takes a sequence file in fasta frmat as input. Fr example, the file shuld lk like > ESGVIWYNEVMHGKS > VWERLGPATSWKTEA > VDVWYSESVHAKPSV > VRGMLPNWYDEMMFS > AANPVEMGLLTMSRL A sample file is prvided in Yu can dwnlad and decmpress the file by running wget tar -xvzf dataset.tar.gz Output files The utputs f ASC+MEME mainly cntain three parts: i. pwms.txt This file cntains the psitin weight matrix (PWM) f every mtif fund. Fr example, MOTIF 1 letter-prbability matrix: alength= 20 w= 8 nsites= 16 E= 8.60e

3 ii. pwm_matched_sequences.txt This file cntains the ccurrences f each mtif. Fr example, iii. #pattern name sequence name start stp strand scre p-value q-value matched sequence e-05 YSYVLLNM e-05 WSGVWYDK e-06 YESVHLHK e-05 YFYEWDSK A directry named lgs This directry includes the lgs f all the mtifs. Examples Assuming yu are currently in the package directry ASC+MEME and there is an input fasta file dataset/input.fa, here are sme examples f running the prgram. Running with default settings If yu are just testing ut the prgram, yu can simply run the prgram with pythn asc_meme.py dataset/input.fa Specify an utput directry pythn asc_meme.py dataset/input.fa - yur_utput_dir

4 Specify the number f clusters By default, ASC+MEME will run recursively t find the right number f clusters (similar t the number f mtifs yu want t find), but this culd slw dwn the running prcess. S, it's better t specify this number. Fr example, pythn asc_meme.py dataset/input.fa -npar 50 Wrking with DNA sequences By default, ASC+MEME uses prtein alphabet. If yu are wrking n DNA sequences, d the fllwing pythn asc_meme.py dataset/input.fa -dna Change the default settings If yu want t change the default settings f the prgram s that yu dn't need t prvide the custmized parameters every time, just change the cntent f the file settings.txt. Optins Fr mre ptins, check the fllwing. usage: asc_meme.py [-h] [-v] [- <utput dir>] [-text] [-dna] [-evt <ev>] [-minsites <minsites>] [-minw <minw>] [-maxw <maxw>] [-bfile <bfile>] [-npar <npar>] [-seql <seql>] [-d <d>] [-maxi maxi] [-ct <ct>] [-mt <mt>] [-cs <cs>] [-pa <pa>] [-nsample <nsample>] [-nseq <nseq>] [-nsmtif <nsmtif>] [-cc] [-re] [-klt <klt>] [-njbs <njbs>] <dataset> The pipeline f ASC+MEME. psitinal arguments: <dataset> ptinal arguments: -h, --help -v, --verbse file cntaining sequences in FASTA frmat shw this help message and exit verbse mde cmmn ptins: - <utput dir> name f directry fr utput files will replace existing directry -text utput in text frmat withut generating pwm lgs -dna sequences use DNA alphabet (default is prtein

5 alphabet) -evt <ev> the threshld f E-value fr a mtif t be significant (default: 0.01) -minsites <minsites> minimum number f sites fr each mtif (default: 10) -minw <minw> minumum mtif width (default: 6) -maxw <maxw> maximum mtif width (default is equal t seql) -bfile <bfile> name f backgrund Markv mdel file asc related ptins: -npar <npar> -seql <seql> number f partitins (if nt specified, the algrithm will run recursively until it reaches terminatin cnditin) length f sequences (if nt specified, the algrithm will set seql equal t the length f the shrtest sequence) -d <d> # f anchrs in each partitin center (default: 5) -maxi maxi maximum # f iteratins (default: 1000) -ct <ct> threshld f cnvergence criteria (default: 0.01) -mt <mt> the threshld f the number f cmmn anchrs t cmbine tw partitins (default: 1) -cs <cs> the cnvergence threshld will be added with this step after each iteratin (default: 0.00) -pa <pa> penalty number fr small partitins (default: 50) pstprcessing ptins: -nsample <nsample> the number f samples fr each partitin (default: 2) -nseq <nseq> the number f sequences per sample (default: 500) -nsmtif <nsmtif> number f mtifs t be discvered per sample (default: 3) -cc find ccurrences fr each mtif -re re-calculate E-value fr each mtif -klt <klt> the threshld f KL value t merge tw mtifs (default: 1.5) -njbs <njbs> number f MEME jbs that are run simultaneusly (default: 25) Cite If yu use this sftware in yur research, please cite the fllwing paper: Fast Mtif Discvery in Shrt Sequences Liu, Hnglei, Fangqiu Han, Hngjun Zhu, Xifeng Yan, and Kenneth S. Ksik Prc. f Int. Cnf. n Data Engineering (ICDE 2016)