Difference between revisions of "LoadExperiment"

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== Add data  ==
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== Add primary data  ==
  
 
[[File:LoadExperiment-data.png|thumb|300px|Select data files]]
 
[[File:LoadExperiment-data.png|thumb|300px|Select data files]]

Revision as of 10:48, 21 September 2016

LoadExperiment enables you to load experimental quantitative, polymorphism, or alignment data for a genome in CoGe. Many common file formats are supported. The data can then be viewed alongside genome sequence/annotation in GenomeView. The LoadExperiment "wizard" guides you through each step:

  1. Describe your experiment
  2. Add data
  3. Select Options
  4. Review and Submit


Metadata: describe your experiment

The first step in loading an experiment into CoGe is to describe it with metadata. CoGe requires a minimum set of metadata fields:

Enter metadata that describes the experiment
  • Name: Name of experiment
  • Description: Description of experiment
  • Version: Version of experiment. Can be a combination of numbers and letters. Exclude the leading "v" as CoGe will add that for you. Set to "1" if you aren't sure what the value should be.
  • Source: Where is the data from? This could be you, your lab, your university, a sequencing center, your collaborator.
  • Restricted: Specify whether the experiment public or restricted to you and your collaborators
  • Genome: Select the appropriate genome from CoGe to associate the data with

Add primary data

Select data files

You can select and retrieve data file located at:

  • The iPlant Data Store
    • CoGe has automatic access to the 'coge_data' directory in your iPlant Data Store. You can place your data to load into that directory, or manually share the data with the "coge" user.
  • An FTP or HTTP URL
  • Your computer (Upload)
    • Not recommended for large files!

Note: Typically only a single data file can be selected. The exception is FASTQ files for which multiple files can be selected.

Data Formats and Track Types

LoadExperiment supports several data file formats depending on the data type:

  • Quantitative data
    Quantitative track
    • Comma-separated (CSV) file format
    • Tab-separated (TSV) file format
    • BED & BedGraph file formats
    • WIG & BigWig file format
  • Marker data
    Marker track
    • GFF/GTF file format
  • Polymorphism (SNP) data
    SNP track
    • Variant Call Format (VCF/GVCF) file format
  • Alignment data
    Alignment track
    • BAM file format
    • FASTQ file format

Each of these file formats are described below in their own separate section. The file type can be auto-detected by LoadExperiment if the file name ends with the expected extension (.csv, .tsv, .bed, .gff, .gtf, .vcf, .bam). Files can be compressed (.zip, .gz) and still have their type auto-detected (e.g., mydata.bed.gz). For non-standard file name extensions, you can force a specific type by selecting it from the list of file types.

CSV File Format

This is a comma-delimited file that contains the following columns

  • Chromosome (string)
  • Start position (integer)
  • Stop position (integer)
  • Chromosome Strand (1 or -1)
  • Measurement Value (real number)
  • Second Value (OPTIONAL): can store a second value such as an expect value (real number)
#CHR,START,STOP,STRAND,VALUE1,VALUE2
1,11486,12316,1,0.181430277220112,7.3980806218146
1,27309,28272,1,0.944373742485446,5.08225285439412
1,32484,32978,1,0.328500324191726,1.97719838086201
1,41942,42508,-1,0.825027233105203,6.56057592312617
1,56394,57527,-1,0.183234367788511,0.795527328556531
1,67705,68809,-1,0.956523086778851,5.20992343466606
1,71144,72409,1,0.42955128220331,1.80604269639474
1,81671,82833,1,0.626003507696723,2.77834108023821
1,86467,87623,-1,0.0878653961575928,7.42843749315945

TSV File Format

Same as CSV format but with tab delimiters instead of commas.

BED & BedGraph File Formats

Note that the 0-based coordinates of the BED format will be translated into 1-based within CoGe.

WIG & BigWig File Format

GFF File Format

Standard GFF3 format as defined here: http://gmod.org/wiki/GFF3

Only the seqid, start, end, score, strand, and attribute columns are used (column numbers 1, 4, 5, 6, 7, 9 respectively).

VCF File Format

Standard VCF 4.1 format as defined here: http://www.1000genomes.org/wiki/Analysis/Variant%20Call%20Format/vcf-variant-call-format-version-41

In short, it is a tab-separated file that is separated into two sections:

  1. Metadata information lines at the top of the file
  2. The sequencing data set containing all of the comparisons of the individual(s) to a reference genome with the following column headings:
    1. Chromosome - name of the scaffold, pseudomolecule, or chromosome the SNP call is located on
    2. Position - where the SNP is called on the chromosome in bp
    3. ID - a semicolon separated list of unique identifiers for the SNP. If there is no identifier a period (".") should be recorded. No white space or semicolons.
    4. Reference - the base that exists on the reference genome (A,G,C,T,N). Multiple nucleotides are shown for indels or complex substitutions.
    5. Alt - comma separated list of nucleotides that are different in an individual from the reference (A,G,C,T,N,*). The asterisk denotes a deletion.
    6. Quality - Phred quality score for the alternate nucleotide call.
    7. Filter - whether the ref-alt pair passed any filters set in analysis. The word 'pass' will be present if the section passes all filters. A blank represented by a period ('.') will show if no filters were used
    8. Info -
    9. Format -
    10. Individuals - can be any number of columns describing each individual in the population of interest

GVCF for Population Genetics Calculations

Chromosome	Position ID	Reference  Alt	  Quality  Filter  INFO	   Format          Indiv1                   Indiv2	
scaffold_1      114      .       A         .       52.8    .       .       GT:DP           0/0:23                   0/0:19  
scaffold_1      116      .       C         T       151.24  .       .       GT:AD:DP:GQ:PL  0/0:23,0:23:54:0,54,392  0/0:17,0:19:24:0,24,231 
scaffold_1      117      .       C         A       704.91  .       .       GT:AD:DP:GQ:PL  0/0:25,0:25:57:0,57,413  0/0:18,2:20:8:0,8,193   
scaffold_1      118      .       T         .       52.83   .       .       GT:DP           0/0:25                   0/0:20  
scaffold_1      119      .       A         .       14.95   LowQual .       GT:DP           0/0:26                   0/0:20  
scaffold_1      120      .       T         G       250.44  .       .       GT:AD:DP:GQ:PL  0/1:16,3:19:8:8,0,72     0/1:29,10:40:77:77,0,318 
scaffold_1      121      .       A         T       49.94   .       .       GT:AD:DP:GQ:PL  0/0:39,0:39:63:0,63,473  0/0:18,0:18:6:0,6,43    
scaffold_1      122      .       C         .       15.89   LowQual .       GT:DP           0/0:28                   0/0:20  
scaffold_1      123      .       C         .       49.89   .       .       GT:DP           0/0:29                   0/0:20

BAM File Format

Standard BAM format.

FASTQ Data

RNAseq reads in FASTQ format.

  • Single-ended: one or more single-ended FASTQ files can be specified.
  • Paired-end: only two paired-end FASTQ files can be specified. The filename corresponding to the 5' reads should end with "R1" and the filename for the 3' reads should end with "R2".
    • Example: Sample1_R1.fastq, Sample2_R2.fastq

Select Options for FASTQ data processing

Select options

This step displays options specific to the type of data that you selected.

The most complicated options are for FASTQ input files. CoGe provides multiple downstream analyses in addition to mapping reads to the reference sequence.

Expression Analysis Pipeline

The Expression Analysis Pipeline developed by James Schnable for his qTeller project.

SNP Identification Pipeline

Multiple methods of SNP identification are available to choose from:

Methylation Analysis Pipeline

Analyze whole genome bisulfite sequencing (WGBS) reads for methylation analysis using the Methylation Analysis Pipeline

The methylation pipeline was developed by Jeff Grover at the University of Arizona.

The metaplot analysis was developed by Josquin Daron and Keith Slotkin at Ohio State University.

ChIP-seq Analysis Pipeline

Analyze chromatin immunoprecipitation sequence using the ChIP-seq Analysis Pipeline.

Review and Submit

This final step simply displays of summary of the metadata, data, and options you've selected. To begin the load click the "Start Loading" button.

Troubleshooting

Make sure that these basic requirements are followed:

  • The file format must be one of those listed above.
  • The chromosome names used in the data files exactly match those for the genome. Mismatches will cause the load to fail.
  • For text files, such as .CSV and .VCF, the newline (EOL) characters must be Unix-compliant (LF character, not CRLF) , see this article. This is sometimes a problem when loading text files created in Excel on Windows OS.

Bulk Loading

Please contact the CoGe Team if you have large numbers of experiments you wish to load and we can help you with the bulk loading.

Demo Data

These data are smaller than "real" datasets, but allow you to learn/test the system quickly (or use for demo's teaching)

  • TODO
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