QuestionMake a power point presentation based on the following research…Make a power point presentation based on the following research report document. Computational analysis of rna-seq and chip-seq for k562 and mcf7 cell lines by usingNext Generation Sequencing technology.BIOINFORMATICS: ADV BIOLOGICAL APPS-1 ABSTRACT:Next-generation sequencing (NGS) has made significant progress in sequencing technology since it allows for high-throughput, low-cost gene sequencing. For wet-lab analysis of NGS data, several NGS systems such as Illumina, Roche, and ABI/SOLiD are utilized, and computational tools like as BWA, Bowtie and Galaxy are used for dry-lab analysis of NGS data. One of the most essential elements of NGS data is its application in early illness detection, particularly in cancer, which was previously impossible with traditional sequencing methods like Sanger sequencing. The computational aspects of K562 and MCF7 are done by using RNA-seq and DNA-seq data analysis. RNA sequencing (RNAseq) has developed as a strong tool and become an essential method for transcriptome profiling investigation of differential gene expression in recent years. transcriptome-wide investigation of differential gene expression and mRNA splicing. Through antibody pull-down, sequencing, and analysis, chromatin immunoprecipitation combined with sequencing (ChIP-seq) is a method for identifying protein-DNA interaction sites, with enrichment ‘peak’ calling being the most crucial analytical step.This report aims to provide comprehensive understanding of NGS, as well as information of how the tools will be used and insight into mutation discovery and integrative analysis of RNA-seq and ChIP-seq case studies of K562 and MCF7.BACKGROUND/INTRODUCTION:NGS (next-generation sequencing) is a technique for identifying the sequence of DNA or RNA to investigate genetic variation linked to illnesses or other biological phenomena. This approach was first commercially available in 2005, and it was dubbed “massively parallel sequencing” because it allowed the sequencing of several DNA strands at once, rather than one at a time as with standard Sanger sequencing by capillary electrophoresis (CE). NGS can identify all mutations that are not detectable with traditional sequencing technologies because researchers can now sequence the entire genome, exome, or transcriptome [3]. Whole-genome sequencing, targeted resequencing, finding of transcription factor binding sites, and noncoding RNA expression profiling are just a few of the applications that these technologies have seen thus far [1]. RNA sequencing (RNA-seq), particularly single-cell RNA sequencing (scRNA-seq), is an important part of high-throughput sequencing that provides biological information on a single tumor cell, analyzes the determinants of intratumor expression heterogeneity, and identifies the molecular basis of many oncological diseases. As a result, RNA sequencing provides crucial information for cancer research and treatment. RNA sequencing will be widely employed for studies on many different forms of cancer as the precision medicine age dawns [11].To detect genome-wide DNA areas bound by proteins, chromatin immunoprecipitation followed by sequencing (ChIP-seq) is utilized. To examine ChIP-seq variability, this method integrates repeated ChIP-seq studies for numerous protein targets on a single cell type to identify cell-specific variable areas in ChIP-seq data. We discovered variable areas in human cell lines K562 and MCF-7 using this technique [12]. MCF-7 was developed in 1973 at the Michigan Cancer Foundation and is still the most widely utilized breast cancer cell line in the world. MCF-7’s fame stems from its exceptional hormone sensitivity, which is achieved by the expression of the oestrogen receptor (ER), making it a perfect model for studying hormone response [13]. The K562 cells were the first immortalized myelogenous leukemia cell line discovered in humans. The erythroleukemia-type K562 cells were obtained from a 53-year-old female chronic myelogenous leukemia patient who was in blast crisis [14].METHODS & MATERIALS:Next Generation Sequencing techonolgy methods: Genome sequencing is one of the most extensively used methods for determining genomic function. New genome sequencing methods have opened a whole new field of genomic research since 2005. The complex sequencing technologies covered include massively parallel signature sequencing, polony sequencing, 454 sequencing, Illumina technology, ion torrent technology, SOLiD DNA sequencing technology, and DNA nanoball sequencing [2].RNA- seq analysis: Workflow of RNA-seq analysis:1.Raw sequence and quality control of NGS data: The two fastq files of paired sequencing data is analyzed by using FastQC. FASTQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) is a widely used FASTQ QC application that summarizes, sequence quality, GC content, read quality by position, adapter content in sequences, reports on tetramer frequencies, and many other features that one would expect to get from raw sequence data (5). The FASTQ format stores DNA sequence data and their associated phred quality scores of each base.2.Read alignment or mapping, which is practically infeasible and complex, is a critical step in NGS analysis (6). In general, for NGS analysis we use BLAT, BWT and BWT2 aligners. BWT and BWT2 are index-based aligners with Burrows Wheeler indexing algorithm. Bowtie 2 is a fast and accurate tool for matching sequencing reads (<200 base pairs) to huge reference sequences.Bowtie 2 is used for gapped, local, and paired-end alignment. Bowtie2 using default parameters to align "K562" and "MCF7" as the paired-reads format. And then use samtools(tools which are designed for Linux or Macs OS system). to filter, sort, and index the uniquely mapped reads. 3. Calling mutations: to call mutations from the aligned files samtools/bcftools are used. The variant calls are generated in VCF format by using bcftools. The mutations were observed through the software IGV (Integrative Genome Viewer) at HBD region with the reference genome of human Hg19.The next step is calculating the gene expressions and differential expressions of fastq files of K562 and MCF7 cell lines for RNA seq datasets. Bowtie2 using default parameters to align "K562" and "MCF7" as the paired-reads format. Before running cufflinks, use samtools to filter, sort, and index the uniquely mapped reads. Then cufflinks (Open-source software tools TopHat and Cufflinks are used to locate genes and analyze high-throughput RNA sequencing (RNA-seq) data) [7] are used to analyze their genes individually which is followed by calculating the differential expressions of "K562" and "MCF7" using cuff diff (a technique for estimating transcript-level expression while adjusting for variability across repeat libraries) [7]. Then the output we get is differential expression gene [DEG].4. Gene Functional Enrichment: For the functional enrichment analysis of DEGs collected DAVID database (Database for Annotation, Visualization, and Integrated Discovery offers a complete set of functional annotation tools for understanding the biological significance of vast lists of genes.) [8] is used. In DAVID database under gene ontology every set of genes is associated to a specific cellular function, biological process, component, or pathway GOTERM_MF_ALL GOTERM_BP_ALL, GOTERM_CC_ALL, and KEGG pathway. It is done by using Functional Annotation Clustering for the analysis. And then by using BINGO [10] from cytoscape is a free, open-source software project that combines biomolecular interaction networks, high-throughput expression data, and other molecular states into a single conceptual framework [9] is used for GO enrichment analysis individually.ChIP-seq analysis: Image transcription text(A) Sample preparation and sequencing DNA Reads Histone modification chip fragmentsSequencing (single-end) Read TF mapping GGTATGCC Nucleosome Open ACATTGGCchromatin TTCAGTGC (B) Computational analysis Read distribution Chromati... Show more Chip-seq analysis workflow:[15]The first step includes collecting of reads from ENCODE portal public database where ChIP-seq data of histone modifications may be downloaded. The sequence reads of FASTQ format of POLR2A ChIP-seq data of K562 and MCF7 cell lines are mapped by suing Bowtie2 or BWA consider indels (insertions and deletions) through gapped alignments, which is useful for long and/or paired-end reads. Reads that correspond to the same genomic locations are filtered as redundant reads after alignment, and the remaining nonredundant reads are utilized for analysis [15].Based on the aggregation of sequenced reads, peak calling algorithms identify "peaks" or locations where the target protein is bound [16]. The commonly used tool for peak calling is MACS2(Model-based Analysis of ChIP-seq is an extensively used approach for finding transcription factor binding locations (MACS)) [17]. The final ouput is returned in BED format.Which is then followed by finding enriched motifs from the sequences based on narrow peaks by using MEME-ChIP. MEME-ChIP is a web tool for analyzing Chipseq peak areas,' which are small genomic regions surrounding reported ChIP-seq'peaks[19]. It will do discovery, enrichment analysis, visualization, binding affinity analysis, and identification of motifs in each set of genomic regions. It employs the motifs discovered by two complementary motif discovery algorithmsMEME and STREMEin subsequent visualization, binding affinity, and identification processes.Integration and visualization of K562 and MCF7 data:The Cancer Genome Atlas (TCGA) project needed a way to show integrated copy number data, expression, mutation, and clinical data, therefore IGV development began in 2007[20].For integrative analysis using reference gene of human hg19 to K562 and MCF7 it is visualized through IGV (At all levels of genome resolution, the Integrative Genomics Viewer (IGV) is a high-performance viewer that quickly processes massive heterogeneous data sets while delivering a smooth and intuitive user experience) [19] at 2chromosomal gene regions that chr9(SMC2) and chr11(HBG1)RESULTS:RNA-seq analysis of K562 and MCF7:A. Quality control results: By using FASTQC the reads for K562 and MCF7 cell lines are ENCFF002DKA, ENCFF002DKE were sequenced and encoded by Sanger/Illumina 1.9. It has the total sequences of 74096905 and sequence length of 100 with 47% of GC content. In that 72-73 bases have the phred quality scores of 30 and has 99.9% of base call accuracy. Image transcription textNew tab X ENCFFOODKA_fastq_gz.gz FastO X + X C O File |C:/Users/sreej/AppData/Local/Temp/Temp1_FastQC_on_data_1_Webpage.zip... Show more Image transcription textNew tab X ENCFFOODKA_fastq_gz.gz FastQ X + X C O File |C:/Users/sreej/AppData/Local/Temp/Temp1_FastQC_on_data_1_Webpage.zip... Show more B. Mapping results:Here is the sequence read alignment summary of ENCFF002DKA, ENCFF002DKE from K562 cell line which is generated by using bowtie 2 with reference to human genome 19 as paired end format.Image transcription textActivities Terminal Fri 11:54 () C Sreeja@RUBY003:/Workstation3/Sreeja/Desktop/Homework File Edit View Search Terminal Help Output: -t/- - time print wall-clock time taken by search phases -un write unp… Show moreIt has overall alignment rate of 87.82%Here is the sequence read alignment summary of ENCFF000HQR, ENCFF000HQP from MCF7 cell line which is generated by using bowtie 2 with reference to human genome 19 as paired end format.Image transcription textActivities _ Terminal The 20:40 Sreeja@RUBY003:/Workstation3/Sreeja/Desktop/Homework File Edit View Search Terminal HelpGreejanRUBYDD3:/Workstation3/Sreeja/Desktop/Homework$ bowtie2 -x ./h… Show moreIt has overall alignment rate of 85.69%C. Mutation results: The aligned data of K562 was explored with Hg19as reference genome at HBD gene region to observe mutations. The mutation (variant calls) is generated in VCF format by using bcftools. The below image is the basic statistics of the variant by bcftools. Then the final output bam file is visualized through IGV. Image transcription textActivities org-broad-igv-ui-Main ~ Tue 20:14 IGV File Genomes View Tracks Regions ToolsHelp Human [GRCh 37/hq190 = |chrll chill:3.232.068 3.237.713 PL.4 P15.3 619.2 15.1 p143p14.2 F12 P112 11.12 041 412.1 412.2 413.1 8132 4134 135 414.1 914.2 Q14.3 921 9… Show moreThere are 1117838 data rows (i.e., number of records), there are 945014 rows with SNP single nucleotide polymorphisms (number of SNPs), the data consists of 172824 indel rows (rows with insertions or deletions), 6087 rows with multiallelic sites, and 118 rows with multiallelic SNP sites. The next step for the continuation is differential expression analysis by using cufflinks and cuffdiff. D. Cufflinks results:Image transcription textActivities Terminal Sun 19:20 () C Sreeja@RUBY003:/Workstation3/Sreeja/Desktop/Homework File Edit View Search Terminal HelpGreeja@RUBY003: /Workstation3/Sreeja/Desktop/Homework$ cufflinks -G … Show moreE. Comparative analysis: Cuffdiff results; Functional enrichment analysis Image transcription textActivities _ Terminal Sun 19:21 Sreeja@RUBY003:/Workstation3/Sreeja/Desktop/Homework File Edit View Search Terminal Help–no-effective- length-correction No effective length correction default: FALSE… Show more Gene Expressions at k562 Expressions at mcf7 Log2(fold_change)ENPP4 0 4.24627 infSLC7A2 0 33.6018 infACSM3 63.6198 1.03925 -5.93586ITGA3 0.0505122 15.0872 8.22247ABCC8 0.00174107 1.28829 9.53126ETV1 6.45453 0.0328102 -7.62003PRSS21 17.8062 0.0136065 -10.3539CYP24A1 0 8.02838 infSNAI2 0 6.24457 infFHL1 0.168407 10.104 5.90683 To assess relative abundances, aligned data from K562 and MCF7 cell lines were assembled into a collection of transcripts. The positive log2 fold change for ABCC8, ITGA3, and FHL1 was 9.53, 8.22, and 5.90, respectively which means increasing expression. While ACSM3, ETV1, and PRSS21 all showed a negative log2 fold change of -5.93, -7.62, and -10.35, respectively which indicates reduced expression, showing that the first group is downregulated when compared to the second.For the enrichment analysis of DEGs collected under the gene ontology, the David data set was utilized. The results of functional enrichment analysis for GOTERM_BP_ALL, GOTERM_CC_ALL, and GOTERM_MF_ALL obtained from the DAVID database show that the list totals 12, 124 and 58 rows, respectively, and the population total is 19349, 20164, 18770 rows, with 26, 24, 5 rows of biological discoveries observed after sorting it based on 0.01 threshold p value.POLR2A ChIP-seq analysis of K562 and MCF7:Mapping results:Image transcription textActivities Terminal Fri 07:52 . () C Sreeja@RUBY003:/Workstation3/Sreeja/Desktop/homework6 File Edit View Search Terminal HelpSreeja@RUBY003: ~$ pwd /Workstation3/Sreeja Sreeja@RUBY003: ~$ Is D… Show moreThis output file contains a total of 30533910 reads, of which 1019550 (3.34 percent) aligned concordantly 0 times, 26094166 (85.64 percent) aligned concordantly exactly 1 time, and 3420194 (11.20 percent) aligned concordantly >1 time. 1019550 pairings were concordantly aligned 0 times, whereas 192040 (18.84 percent) were discordantly aligned 1 time. 827510 pairings aligned 0 times concordantly or discordantly; the pairs are made up of 1655020 mates; of these, 1086314 (65.64 percent) aligned 0 times and 314260 (18.99 percent) aligned exactly 1 time. With an overall alignment rate of 98.22 percent, 254446 (15.37 percent) aligned >1 time. Image transcription textActivities Files Sun 19:18 . Trash Sreeja@RUBY003:/Workstation3/Sreeja/Desktop/homework6 File Edit View Search Terminal HelpENCFFOOZEFA. fastq. gz K562_POLRZA_R2_ENCFF698ICA. fastq.gz Hom… Show moreThis output file contains a total of 30298258 reads, of which 30298258 (100%) were unpaired with an alignment rate of 97.66 percent, with 710125 (2.34%) aligned 0 times and 21228244 (70.06%) aligned exactly 1 time and 8359889 (27.59%) aligned >1 times.Macs2 peak calling results: On the aligned data for the k562 and MCF7, the tool MACS2 was employed. To improve the specificity of peak calling, the two raw reads ENCFF839IPL and ENCFF698ICA from k562 POLR2A ChIP-seq data were compared to the control ENCFF002EFD_EFA. Using a tag size of 101bps, a total of 55392 narrow and broad peaks were discovered. In the same way, the MCF7 cell line had a total of 55392 peaks with a tag size of 36bps. The raw reads ENCFF000SBI from MCF7 POLR2A ChIP-seq data for library ENCLB469RNT were utilized in this study. And it was compared to the control sample, which was the MCF7 control data raw read ENCFF000SA2.Motif analysis:It was now necessary to use the tool MEME-CHIP to extract the MOTIFS from the ChIP-seq data. To locate the motifs, the sequences were extracted based on the narrow peaks. The significant motifs with E-value<0.05 found by the programs MEME, STREME and CentriMo clustered by similarity and ordered by E-value.algorithm K562 motifs MCF7 motifs MEME Image transcription text... Show more Image transcription text... Show more STREME Image transcription text... Show more Image transcription text... Show moreMEME-ChIP motif output for K562:MOTIF_INDEX MOTIF_ID CONSENSUS WIDTH SITES E-VALUE MOST_SIMILAR_MOTIF1 WWTTTTTKTWTTTTT WWTTTTTKTWTTTTT 15 7483 0 MA0679.2 (ONECUT1)2 GCTGGGATTACAGGC GCTGGGATTACAGGC 15 4953 0 3 TGTTGSCCAGGCTGG TGTTGSCCAGGCTGG 15 5479 2.3E-284 MA1973.1 (ZKSCAN3)4 1-GCCTCRGCCTCCCRA GCCTCRGCCTCCCRA 15 5242 4.5E-139 MA1596.1 (ZNF460)5 2-CTGTAATCCCAGCW CTGTAATCCCAGCW 14 4049 2.3E-134 MA1929.1 (CTCF)6 3-GRGYGYRGTGGCDCR GRGYGYRGTGGCDCR 15 5094 3.1E-123 UP00007_1 (Egr1_primary)7 4-CAGCCTGGSCAACA CAGCCTGGSCAACA 14 3933 1.9E-101 MA1727.1 (ZNF417)8 5-TWGAGAYRGRGTYTC TWGAGAYRGRGTYTC 15 3789 3.5E-95 UP00082_2 (Zfp187_secondary)9 6-TCAAGYGATYCTC TCAAGYGATYCTC 13 3348 9.9E-91 10 7-ATTTTTGTATTTTTA ATTTTTGTATTTTTA 15 1734 1.7E-53 MA0757.1 (ONECUT3)11 8-GCAGTGAGCCRAGAT GCAGTGAGCCRAGAT 15 2215 2.9E-52 MA1643.1 (NFIB)12 9-ACCCRGGAGGCGGAG ACCCRGGAGGCGGAG 15 945 2.4E-16 MA1596.1 (ZNF460)13 10-CCTCGTGATCCRCC 10-CCTCGTGATCCRCC 14 523 1.5E-11 14 11-AAAAAAAAAAASAAA 11-AAAAAAAAAAASAAA 15 1239 2.3E-10 MA1125.1 (ZNF384)15 12-CAGGAGTTCRAG 12-CAGGAGTTCRAG 12 534 5.4E-09 MA1587.1 (ZNF135)16 13-AAAAATTAGCY 13-AAAAATTAGCY 11 455 0.000000012 17 14-CCCCGCCCCC 14-CCCCGCCCCC 10 1764 0.000000062 MA1511.2 (KLF10)18 15-GAGYRAGACTCTSTC 15-GAGYRAGACTCTSTC 15 387 0.00000027 MA0014.3 (PAX5)19 16-GAGGCKGMGGCRG 16-GAGGCKGMGGCRG 13 808 0.0000033 MA1596.1 (ZNF460)20 17-CCGTCTCTAC 17-CCGTCTCTAC 10 255 0.0000035 21 18-GCRTGAGCCAC 18-GCRTGAGCCAC 11 398 0.00009 22 19-CGCCTGTAATCC 19-CGCCTGTAATCC 12 233 0.0021 23 20-ACCATGTTGGCCA 20-ACCATGTTGGCCA 13 296 0.0031 24 21-ATCACCTGAGG 21-ATCACCTGAGG 11 155 0.0034 MA0829.2 (SREBF1)25 22-ACTCGGGAGGC 22-ACTCGGGAGGC 11 167 0.0041 MA1596.1 (ZNF460)26 23-AACCTCCGCCT 23-AACCTCCGCCT 11 163 0.0041 MA1708.1 (ETV7)27 24-CTTTGGGAGGC 24-CTTTGGGAGGC 11 142 0.014 UP00047_1 (Zbtb7b_primary)28 25-CRGCTCACTGCA 25-CRGCTCACTGCA 12 130 0.015 MA0038.2 (GFI1)29 26-AAAGAAAA 26-AAAGAAAA 8 778 0.026 30 27-ATCTCGGCTCA 27-ATCTCGGCTCA 11 74 0.028 31 28-GCCYGGCTA 28-GCCYGGCTA 9 404 0.042 MA1979.1 (ZNF416)In the K562 cell line, 31 motif sequences were revealed that appeared regularly and had some biological purpose. Each of these motifs had varied widths, and the predicted number of occurrences in the motif's training set (sites-The number of "Total Matches" recorded by CentriMo, or the number of sites reported by the de novo program.) was also different from each other.MEME CHIP motif output for MCF7:MOTIF_INDEX MOTIF_ID CONSENSUS WIDTH SITES E-VALUE MOST_SIMILAR_MOTIF1 TTTDTWTTTTTTKTW TTTDTWTTTTTTKTW 15 959 1.3E-33 MA1274.1 (DOF3.6)2 1-CTCVGCCTC CTCVGCCTC 9 13508 1.5E-14 MA1596.1 (ZNF460)3 2-AAGATGGCGGCS AAGATGGCGGCS 12 896 1.8E-14 MA0748.2 (YY2)4 3-TGHGCCACYRCRCY TGHGCCACYRCRCY 14 520 5.3E-09 MA1531.1 (NR1D1)5 4-CCAGCCTGGGCRACA CCAGCCTGGGCRACA 15 346 8.4E-09 6 5-ATCRCTTGAGCCCAG ATCRCTTGAGCCCAG 15 385 0.00000028 MA1581.1 (ZBTB6)7 6-GCSGCSGCSGCSGC GCSGCSGCSGCSGC 14 2108 0.00000044 MA1833.1 (Zm00001d049364)8 7-RCCTGTARTCCCAGC RCCTGTARTCCCAGC 15 514 0.00000055 9 8-GCGCATGCGCA GCGCATGCGCA 11 383 0.00000067 MA0506.2 (Nrf1)10 9-TGCAGTGAGCYRWGA TGCAGTGAGCYRWGA 15 298 0.0000045 MA0781.1 (PAX9)11 CAGGCTGGAGTGCAG CAGGCTGGAGTGCAG 15 158 0.000016 MA1121.1 (TEAD2)12 10-CACTTCCGGC 10-CACTTCCGGC 10 411 0.00015 MA0076.2 (ELK4)13 YBGCTGCTGCT YBGCTGCTGCT 11 849 0.00093 MA1815.1 (GRF4)14 11-GTCCCCA 11-GTCCCCA 7 3477 0.002 MA1282.1 (TCP13)15 12-GTCACGTGAYC 12-GTCACGTGAYC 11 213 0.0034 MA0692.1 (TFEB)16 13-GCCTCAGTTTCCYY 13-GCCTCAGTTTCCYY 14 134 0.0047 MA1596.1 (ZNF460)17 14-ACACAYRCACRCAC 14-ACACAYRCACRCAC 14 389 0.012 MA0538.1 (daf-12)In the MCF7 cell line, there were 17 different motifs found. Each of these motifs had varied widths, and the predicted number of occurrences in the motif's training set (sites-The number of "Total Matches" recorded by CentriMo, or the number of sites reported by the de novo program.) was also different from each other.Integrative analysis of RNA-seq and ChIP-seq for case study of K562 and MCF7:1)By using reference gene of human hg19 to K562 and MCF7 it is visualized through IGV at chr9:106840000-106920000 (SMC2 gene region). Here are the images of K562 and MCF with reference to hg19 at SMC2 gene region. For K562: Image transcription textActivities Dry-broad-jus-ui-Main - Sun 20305+ Hle benames view frocks Regions Tools HelpHuman IGHChihuly ~ Jup TT TITIT TT A TH THAT HIT THFor MCf7: For RNA seq track of K562 and MCF7 cell line we can observe 2 peaks and the mismatched bases with the given reference base are A:100% at positions chr9:106857861-chr9:106880541 and chr9:106878596 and the colored region which indicates higher quality with lower mismatch with bases and here cytosine:39 &69 quality scores some of them observed at chr9 region which is a type of indel (insertion or deletion) & SNP with a mapping quality of 27, 41 and depth read is 21,40.For POLR2A track of K562 and MCF7 cell line we can observe 2 peaks and the mismatched bases at 2 levels with the given reference base are G:100%, A:100% at positions chr9:106856322-chr9:106856556 and chr9:106872230-chr9:106882352 and we can observe some colored regions which indicates higher quality with lower mismatch with bases and there are C:40, T:37quality scores some of them observed at chr9 region. And there is only one peak observed for K562 summits bed track at position chr9:106856193-106856193 that is K562_POLR2A_peak_52790 with score 166.982 but for MCF7 there is no peaks at summit bed track.2)With reference gene of human hg19 to K562 and MCF7 it is visualized through IGV at chr11:5250000-5282000 (HBG1 gene region). For K562: Image transcription textActivities . org-broad-Igv-ul-Main File Genomes View Tracks Regions Tools Help ICY Human[GRCh37hg1% = chrll - chil:5 264.787 5.277.521 HIS 4 41 424.4 18 kt 5.272 LL igv ITTT BZITAFor MCF7: For RNA seq track of K562 and MCF7 cell line we can observe 2 peaks and the mismatched bases with the given reference base are G:100%, G:100% at positions chr11:5269587-chr11:5274496 and chr11:5269799-chr11:5270350 and colored regions which indicates higher quality with lower mismatch with bases and here are C:38, T:35 quality scores some of them observed at chr11 region which is a type of INDEL and SNP.For POLR2A track of K562 & MCF7 cell line we can observe 2peaks and the mismatched bases at 2 levels with the given reference base are T:100% at positions chr11:5264914-chr11:5273695 and chr11:5270335 colored regions which indicates higher quality with lower mismatch with bases and there are A:41, T:40,41 quality scores some of them observed at chr11 region.And there are 2 peaks observed for K562 summits bed track at position chr11:5270392-570392, chr11:5271320-5271320 that is K562_POLR2A_peak_8368, K562_POLR2A_peak_8369 with scores 11.0184 and 28.4872 but there no peaks for MCF7 at summit bed track.Discussion and conclusion:While processing NGS data, a vast number of intermediate analysis files and result files are created. In terms of turning data to knowledge for clinical applications, interpreting these complicated NGS data sets, particularly for aggregated vast volumes of variants or heterogeneous sequencing data, is difficult. For quality control output both K562 and MCF7 has 99.9%base accuracy with 30 phred quality scores. By using RNA-seq mapping results for both cell lines have overall alignment rate of 87.82% and 85.69%. The next step for the continuation is differential expression analysis by using cufflinks and cuffdiff.When applied to transcription factor ChIP-seq data, this test gives a reliable repeatability measure across peaks coming from different biological samples.31 and 17 motifs are observed for K562, MCF7 cell line by using MEME-ChIP. When visualizing in IGV the narrow peaks for K562 cell lines are observed at chr9 and chr11 region but for MCF7 it doesn't show any narrow peaks for summit bed track at chr9 and chr11.References:Morozova, O., & Marra, M. A. (2008). 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