Use the first two plots to select a chromosome and one or more varieties.
Click on data points in the plot to see field trial data.
More Field Trial Data by Year
Maturity, Lodging, Seeds
Yield, Protein, Oil Pairwise Plots
This section details the data collection and analysis process used to generate the results shown in this application.
 Use the Search field above the table to filter by Glyma ID.
Type the chromosome number or click on the text box for options
Type the name of the variety or click on the text box for options

Select the types of CNV regions you would like to display
The first plot ('Whole-Genome CNV Density') shows normalized CNV counts for all varieties over all chromosomes. Click on a chromosome region to view more detailed information for that chromosome.
The second plot ('Number of CNVs by Variety') shows CNV counts for each variety, sorted from most CNVs to fewest CNVs. Click on one or more varieties to see more detailed information about which regions of the chromosome contain the most CNVs.
The third plot ('Density of CNVs') shows the overall CNV distribution across all varieties in black, with selected varieties (from the top-right plot) shown in color.
The last plot ('Difference in Normalized CNV Count') shows, for each selected variety, which regions of the chromosome contain relatively more CNVs than the average variety, and which regions of the chromosome contain relatively fewer CNVs.

Green lines indicate a significant CNV at that location

Open circles indicate a significant CNV at that location; darker blue lines indicate higher copy number, lighter lines indicate lower copy number.
CNV Analysis Documentation

Data Collection

The data displayed in this applet is derived from 79 lines of soybean next-generation sequencing data. Twenty seeds from each line were acquired from ---. Seeds were planted in the USDA greenhouse at Iowa State University. Once plants reached the trifoliolate stage, leaves from up to 10 plants were pooled and genomic DNA was extracted. DNA was sent to Hudson Alpha Institute for Biotechnology for next-generation sequencing. In addition, replicated field trials were conducted on a subset of lines (30 of the 79 lines, plus ancestral varieties that were not sequenced) to measure protein, oil, yield, and other characteristics under standard growth conditions, to dissociate the effect of on-farm improvements from genetic gain [1],[2].

Processing Data

1. Raw reads were aligned to the reference soybean genome (version 2) using GSNAP (version 2013-8-31) [3].
2. Reads that mapped uniquely were converted from SAM format to BAM format using samtools [4].
3. Read groups were added for each soybean line and duplicate reads were removed using AddOrReplaceReadGroups and MarkDuplicates functions in picard tools.
4. The resulting alignment BAM files were realigned using IndelRealigner function in GATK [5]. The ReduceReads function was used to compress the alignment files by removing non-informative and redundant reads (default parameters except for downsample_coverage=1).

Copy Number Identification

Using BAM files from the previous step as input to cn.mops, the program was executed separately on each geneomic feature (gene, exon, CDS, mRNA) to provide internal verification as well as reduce the problem to a more computationally manageable size. As suggested in the cn.mops manual, each region of the genome was extended by 30 bp on each side to aid in identification of CNV regions.

After running cn.mops, results were assembled and merged with annotation files.

The results from the algorithm were back-transformed (regions were reduced by 30 bp on each side) and merged with annotation files.

Plotting Data

Plots in this applet were generated using ggplot2 [6], and are rendered interactively using Shiny [7].

References

1. Specht JE, Williams JH. Contribution of genetic technology to soybean productivity—Retrospect and prospect. Genetic contributions to yield gains of five major crop plants. Crop Science Society of America; American Society of Agronomy; 1984;49–74.

2. Fox CM, Cary TR, Colgrove AL, Nafziger ED, Haudenshield JS, Hartman GL, Specht JE, Diers BW. Estimating soybean genetic gain for yield in the northern united states—Influence of cropping history. Crop Science. The Crop Science Society of America, Inc. 2013;53:2473–2482.

3. Wu TD, Nacu S. Fast and snp-tolerant detection of complex variants and splicing in short reads. Bioinformatics. Oxford Univ Press; 2010;26:873–881.

4. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, others. The sequence alignment/map format and samtools. Bioinformatics. Oxford Univ Press; 2009;25:2078–2079.

5. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, others. The genome analysis toolkit: A mapreduce framework for analyzing next-generation dna sequencing data. Genome research. Cold Spring Harbor Lab; 2010;20:1297–1303.

6. Wickham H. Ggplot2: Elegant graphics for data analysis [Internet]. Springer New York; 2009. Available from: http://had.co.nz/ggplot2/book.

7. RStudio, Inc. Shiny: Web application framework for r [Internet]. 2014. Available from: http://www.rstudio.com/shiny/.