등록 파이프라인

No 분석 분야 파이프라인 이름 설명 상세 정보
1 Epigenome Analysis Pipeline POSTECH_EPIGENOME_SEQUENCING_FASTQC_BOWTIE_MACS_PIPING 각 단계에서 진행되는 분석 과정은 다음과 같다. Quality control 단계에서 입력 데이터의 sequencing quality를 확인한다. Quality filter 단계에서 데이터 중 quality가 낮은 reads를 제거한다. Alignment 단계에서 참조 서열에 기반 해 데이터를 mapping 한다. Cross correlation 단계에서 그 결과에 대해 quality control을 한다. Peak calling 단계에서 유의미한 부위인 peaks를 탐색한다. 이 때, MACS을 사용한다. Annotation 단계에서는 앞 단계에서 찾은 부위들에 대한 상세한 설명을 덧붙인다. Visualization 단계에서는 mapping 데이터와 peaks 데이터를 시각화 한다. 상세보기
2 Epigenome Analysis Pipeline POSTECH_BROAD_SOURCE_CHIP_SEQ_FASTQC_BWA_MACS2_PIPING 각 단계에서 진행되는 분석 과정은 다음과 같다. Quality control 단계에서 입력데이터의 sequencing quality를 확인한다. Quality filter 단계에서 데이터 중 quality가 낮은 reads를 제거한다. Alignment 단계에서 참조 서열에 기반 해 데이터를 mapping 한 후 Mapping이 끝난 데이터의 Mapping Quality 및 duplication level을 확인한다. Visualization 단계에서는 mapping 데이터와 peaks 데이터를 시각화 한다. Peak calling 단계에서 broad-source factor에 특화된 RSEG/SICER/hiddenDomains/BCP/MACS2를 이용해 유의미한 부위인 peak(또는 domain)를 탐색한다. Annotation 단계에서는 앞 단계에서 찾은 부위들에 대한 상세한 설명을 덧붙인다. 상세보기
3 RNA Sequencing Pipeline RNASeq_TOPHAT2_CUFFLINKS_PIPING This pipeline Analyze and processes RNA_seq sample _then it assembles transcripts_ estimates their abundances_ and tests for differential expression and regulation in RNA_Seq samples using CUFFLINK_ 상세보기
4 RNA Sequencing Pipeline RNASeq_STAR_RSEM_PIPING This pipeline is an RNA sequencing pipeline that aligns with the STAR program and performs quntification with RSEM 상세보기
5 RNA Sequencing Pipeline RNASeq_STAR_HTSEQ_PIPING This pipeline is an RNA sequencing pipeline that aligns with the STAR program and performs quantification with HTSeq 상세보기
6 RNA Sequencing Pipeline RNASeq_KALLISTO_PIPING This pipeline is an RNA sequencing pipeline that performs pseudo alignment and quntification quickly using the Kallisto program 상세보기
7 RNA Sequencing Pipeline RNASeq_EMSAR_PIPING This pipeline Analyze the RNA_seq to get isoform_level esitmates by EMSAR_ and then it will give you gene_level expression level estimates using isoform_level esitmates 상세보기
8 ETC Pipeline COLLECTIVE_GENOME_PCA_KIMURA_PIPING First_ convert the VCF file to Plink format_ Second_ convert the PED file of the converted Plink format file into the Fasta format file_ Third_ we use this modified Fasta file to generate a Pari_wise matrix of Kimura two parameter distances for all samples_ Fourth_ PCA and PCE plots for PC1 and PC2 are generated using SVD singular value decomposition using the generated pairwise matrix_ Finally_ the phylogenetic tree is drawn using the BioNJ algorithm_ which is an improved version of the neighbor joining algorithm_ using the pairwise matrix_ and then a Newick form that can be additionally used for MEGA7 is generated 상세보기
9 ETC Pipeline COLLECTIVE_GENOME_BETWEEN_GROUPS_PI_CALCULATION_PIPING First_ calculate the Pi value according to the window size and step to be calculated by using the VCF file of the group_ Second_ we calculate the basic statistic of Pi value calculated for each group_ Third_ check whether Pi values are abnormal in order to integrally visualize Pi values calculated for each group_ Fourth_ the Pi value calculated for each group is converted into a file for visualization_ Fifth_ the converted files are merged into the final input file for visualization_ Finally_ visualize the Pi values for each group using the final input_ 상세보기
10 ETC Pipeline COLLECTIVE_GENOME_BETWEEN_GROUPS_FST_CALCULATION_PIPING First_ using the whole VCF file_ input each sample list in the VCF of each group to be calculated_ and calculate the Fst value between the two groups by the desired Window size and step_ Second_ after calculating the basic statistic of the calculated Fst value between the two groups_ the Fst value is normalized and the P_value is calculated_ Third_ the Fst value between the two normalized groups is converted into a final input file for visualization_ Finally_ a Manhattan plot of normalized Fst values for the two groups is generated using the final input_ 상세보기