Following the "Import ChIP-chip data" tutorial, this movie shows how to analyze ChIP patterns with motif sequences of TF binding sites. [related movies] Import ChIP-chip data (Affymetrix BAR format) http://www.screencast.com/t/MIyMqgeUdeP Analyze ChIP-chip data (Affy BAR format) http://www.screencast.com/t/WSBwRyzPK

Following the "import Affy BAR 1" movie, this shows example of analysis flow.

Genomic location filter mediates different types of omics data, like ChIP-seq and expression array data.

Following the "Import RNA-seq (SOLiD, BED) data" movie tutorial, this shows how to visualize and analyze the data.

This is a demo analysis of GSE11431, ChIP-seq data with Subio Platform and plug-ins. http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE11431 SOA file, which you can easily import into Subio Platform to view the data by yourself, is available from the user forum of Subio's web site. http://www.subio.jp/forum/detail/id/433

You can extract genes which are differentially expressed among groups by ANOVA.

Hierarchical clustering is useful to group genes and samples by gene expression profiles.

There are several ways of gene-level-quantification for Affymetrix 3'-IVT GeneChip data. MAS5 is the oldest algorithm, and RMA is one of lately developed ones. Some people say MAS5 and RMA data are not comparable. Some people say RMA is more reliable than MAS5. And others say RMA is a trick to make data quality good. But no algorithm is free from biasing. What's important is to know character of them so that you can select proper one for your purpose. You can download the SSA file to examine by yourself. SSA Download: https://www.sugarsync.com/pf/D664412_7039661_700749 Data Source: "Inflammation, adenoma and cancer: objective classification of colon biopsy specimens with gene expression signature." http://www.ncbi.nlm.nih.gov/projects/geo/query/acc.c​gi?acc=GSE4183

You can extract DEG (Differentially Expressed Genes) by fold and/or p-value. P-values can be corrected by BH FDR.

Enrichment Analysis tool helps your interpretation of a gene list, which shows a specific expression pattern.

You can filter out genes by flag or signal values to remove noise disturbing statistical analysis.

Find miRNA Targets is a tool included in Annotation Analysis Plug-in. This is a demo movie to extract potential target genes of specified miRNAs from two corresponding data sets of gene and miRNA expression profiles.SSA files, which you can easily import into Subio Platform to view the data by yourself, are available from the user forum of Subio's web site.http://www.subio.jp/forum/detail/id/431

You can extract genes which are correlated or anti-correlated to a template pattern.

You can visualize and search genomic location associated data like ChIP-chip/-seq, CGH and tiling array.

You can extract genes of upstream, downstream or overlapping from genes, probes or sequence tags.

Gene Ontology analysis is very popular to characterize a gene set, which is extracted by a statistical analysis.

You can easily select genes by pattern, but how do you get information about those genes? Subio Platform is flexible so that users can define web links for further examination.

This movie shows whole process of importing microarray data of dye-swap samples into Subio Platform. You can omit most part in this movie from the second time of importing data.

This movie shows whole process of importing Affymetrix data into Subio Platform. You can omit most part in this movie from the second time of importing data.

Thise movie shows how to import Affymetrix Gene ST or Exon ST array. Different summarizations need different normalizaions in Subio Platform.

This movie shows whole process of importing CGH array data into Subio Platform. You can omit most part in this movie from the second time of importing data.

This movie shows whole process of importing Agilent 1 color microarray data into Subio Platform. You can omit most part in this movie from the second time of importing data.

Subio Platform v1.12 suports Affymetrix BAR format, which is for tiling array or ChIP-chip data. This tutorial shows how to import BAR files into Subio Platform. Data source: ChIP-chip analysis of human DREAM complex subunits in G0-arrested and S-phase synchronized cells. Contributed to GEO by Zhu X, Liu XS, Sadasivam S, Litovchick L, DeCaprio JA. http://www.affymetrix.com/support/technical/sample_data/tiling_array_data.affx GSE7516: ChIP-chip analysis of human DREAM complex subunits in G0-arrested and S-phase synchronized cells http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE7516

This movie shows whole process of importing ChIP-Seq data based on next generation sequencing technology into Subio Platform. You can omit most part in this movie from the second time of importing data.

Subio Platform directly accepts GEO SOFT format files. You can create Platform and Samples from one SOFT file. Re-analyzing public data is essential part of new life science.

A tutorial movie showing how to import NimbleGen CGH 1.4M data files in GFF format into Subio Platform, and example analysis work flow. For more information about NimbleGen's CGH array: http://www.nimblegen.com/products/cgh/index.html

A tutorial movie showing how to import NimbleGen DNA Methylation array data files in GFF format into Subio Platform, and example analysis work flow. For more information about NimbleGen's DNA Methylation array: http://www.nimblegen.com/products/methylation/index.html

This movie shows whole process of importing NimbleGen microarray data into Subio Platform. You can omit most part in this movie from the second time of importing data.

Subio Platform v1.12 accepts BAM/SAM format files. This tutorial shows how to import BAM files and how to visualize RNA-seq data with Subio Platform. Data Source: GSE29155 RNA-Seq anlalysis of prostate cancer cell lines using Next Generation Sequencing http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29155

In RNA-seq studies, bed files are output after mapping reads. Subio Platform accept bed files for visualization and comparison among samples. Data source: GSE 27443 RNA-Seq of rRNA depleted circulating endothelial progenitor cells (cEPCs) in trisomy 21 and euploid samples http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27443

The NGS generates huge data and you need a high spec computera with a very large storage and RAM. But, of course, only limited biologists are taking such an advantage. Recently, more and more NGS data are submitted to GEO database, how to make use of them? NCBI provides NGS data in SRA format, though many analysis software takes FASTQ files as inputs. If you have some skills, it's not very difficult to extract FASTQ files from an SRA file, but it's not easy for many biologists. In this movie tutorial, I show you how to get FASTQ files of a series which you find in GEO, and analyze them without high spec machines nor difficult software tools. Finally you can get results of peak calling files, which data size are significantly small and you can handle them on any PC or Mac, to import Subio Platform for biological exams.

An ssa file contains all information to reconstruct in Subio Platform on other PCs. It's the easiest way of omics data sharing ever.

Drag on Line Graph to select genes, which are also selected in the annotation table below at a same time.

Drag on Line Graph to select genes, which are also selected in the annotation table below at a same time.

Coordinate blocks to define normalization. You can promptly recall a saved normalization scheme.

Pathway Edit Tool enables users creating and modifying graph elements on pathway image.

Pathway View displays expression pattern on metabolic or signaling pathways. It helps biological interpretation.

PCA is useful to summarize expression profile, especially for researching pathology and diagnosis.

RNA-seq on Illumina GA system might be problematic, and this PolyA-seq approach seems to be better. As authors mentioned, it is likely to be more sensitive than Affymetrix GeneChip data, though GeneChip is still advantageous in cost, automation and data handling. You can download SSA files of these two data sets. GSE30198.ssa (PolyA-seq) https://www.sugarsync.com/​pf/D664412_133640_6678997 GSE2361.ssa (GeneChip) https://www.sugarsync.com/​pf/D664412_133640_6678983 Data Source: http://www.ncbi.nlm.nih.go​v/geo/query/acc.cgi?acc=GS​E30198 Summary: We describe PolyA-Seq, a strand-specific method for high-throughput sequencing of the 3' ends of polyadenylated transcripts. PolyA-Seq is as accurate for digital gene expression as existing RNA sequencing approaches, and superior to microarrays. We used the approach to map polyadenylation (polyA) sites in 24 samples from normal tissues in human, rhesus, dog, mouse, and rat. http://www.ncbi.nlm.nih.go​v/geo/query/acc.cgi?acc=GS​E2361 Summary: We performed expression profiling of 36 types of normal human tissues and identified 2,503 tissue-specific genes. We then systematically studied the expression of these genes in cancers by re-analyzing a large collection of published DNA microarray datasets. Our study shows that integration of each gene's breadth of expression (BOE) in normal tissues is important for biological interpretation of the expression profiles of cancers in terms of tumor differentiation, cell lineage and metastasis. Twenty five total RNA specimens were purchased from Clontech (Palo Alto, CA), Ambion (Austin, TX) and Strategene (La Jolla, CA). We tried to cover as many tissue types as possible by using pooled RNA samples. In order to define breadth-of-expression (BOE) accurately at a reasonable cost, we tried to cover as many tissue types as possible by using pooled RNA samples. Each specimen represents a human organ. We used RNA samples pooled from 2 to 84 donors to avoid differences at the individual level. Keywords = Normal human tissues Keywords = Tissue-specificity

Is RNA-seq data really "digital"? Is it more sensitive or reliable than microarrays? Before you go, I recommend you assess technological bias, limitation and cost-performance with publicly available data. not on the vendor's champion data. Data Source: GSE29155 RNA-Seq anlalysis of prostate cancer cell lines using Next Generation Sequencing http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29155

Scatter Plot (Samples) visualizes a result of PCA. Colors of spots and arrows help your understanding.

Compare data distribution of two samples. You can easily assign samples to axes by drag & drop.

A result of searching interactions is shown in a simple table so that you can save as Excel sheet.

Subio Platformは、Rなどの無料のリソースを活用すると、完全に無料でデータ解析ができるしくみになっています。Rのプログラミングができないという方でも心配いりません。R東大農学部　門田先生のウェブページからRスクリプトをコピペするだけで統計処理が実行できる方法があります。

A tree view visualizes a result of hierarchical clustering. You can easily traverse nodes by navigation buttons.

Drag & drop a Measurement List on circles, then click arbitral area of extracting genes.