8 research outputs found
What are Ecotypes Adapted to? Insights from Common Garden Experiments Across Geographic Gradients
No full text<div>Presentation from Ecological Society of America Annual Meeting Aug 9th, 2017, Portland, Oregon. This presentation was in the IGNITE format and part of a session "<i>Multiple Common Garden Experiments for Meeting Restoration Challenges: Difficulties and Potential Challenges</i>."</div><div><br></div><div>The slides in the actual presentation may be slightly updated from these. </div><div><br></div><div>Abstract: Common garden experiments can be used to recognize ecotypes from across the range of a species with a view to identifying sources suitable for restoration. However, simply growing plants from different sites in one location provides limited insight into the drivers of ecotypic adaptation. We contrast inferences drawn from a multi-garden common garden experiment, along with a common garden experiment that included soil manipulation to highlight the need to manipulate more than just source populations to determine mechanisms behind ecotypic development.</div><div><br></div
Additional file 1: Table S1. of Inferential considerations for low-count RNA-seq transcripts: a case study on the dominant prairie grass Andropogon gerardii
No full textNumber of high quality reads by ecotype and population for 454 and HiSeq platforms. Figure S1. Workflow diagram of transcriptome assembly pipeline. Figure S2. Cumulative length of sequences and number of sequences for various k-mer values, 454 data, and the combined 454 and HiSeq data. Figure S3. N values for various k-mers and MIRA 454 and MIRA clustered assemblies. Figure S4. Ortholog hit ratio for final MIRA clustered assembly. OHR is the length of the BLASTX hit region divided by the length of the protein, in our case using the S. bicolor database. OHR is an estimate of the percent of the full length protein sequence represented in the assembly. An OHR of 1 indicates a potential full length transcript. (DOCX 230Â kb
Multi-k-Mer de novo Transcriptome Assembly, Validation, and Count Summarizing for Four Plant Taxa
No full text<p>Large genomes, polyploidy, and repetitive DNA are common obstacles in the assembly of plant genomes making de novo transcriptomes valuable genomic resources. To generate high quality de novotranscriptomes, we developed a custom assembly workflow for illumina and 454 RNA-Seq reads. The primary components of the workflow include stringent pre-cleaning; Oases multi-k-mer assembly for Illumina reads; MIRA assembly for 454 reads; and MIRA to cluster resulting contigs. We tested the workflow using data from four transcriptomes, two polyploid monocots and two dicots. Assemblies were validated using number of contigs, cumulative length of contigs, and N50 metrics. Ortholog hit ratios (OHR=length of alignment:length of proteins from a close relative) were calculated to estimate assembly fragmentation. Each clustered assembly had a high N50 (2.6-3.2 kb) and a high percentage of hits with an OHR >= 0.8 (52-65%) suggesting the workflow produced high quality assemblies.For de novo transcriptomes, there are few standalone programs that summarize aligned read counts for input into EdgeR or DeSeq. Although popular, HTSeq allows partial length single-end alignments but does not allow alignment of one out of two mates. We developed the custom script Count_reads_denovo.pl, for de novo RNA-Seq projects. Count_reads_denovo.pl uses a model similar to featureCounts, a reference-based summarizer, to leverage paired-end data even where only one mate aligns. The script filters read counts by mapping quality (MAPQ). Scripts used in the above workflow, as well as Count_reads_denovo.pl are available at https://github.com/i5K-KINBRE-script-share/.</p
Multi-k-Mer de novo Transcriptome Assembly, Validation, and Count Summarizing for Four Plant Taxa
No full text<p>Large genomes, polyploidy, and repetitive DNA are common obstacles in the assembly of plant genomes making de novo transcriptomes valuable genomic resources. To generate high quality de novotranscriptomes, we developed a custom assembly workflow for illumina and 454 RNA-Seq reads. The primary components of the workflow include stringent pre-cleaning; Oases multi-k-mer assembly for Illumina reads; MIRA assembly for 454 reads; and MIRA to cluster resulting contigs. We tested the workflow using data from four transcriptomes, two polyploid monocots and two dicots. Assemblies were validated using number of contigs, cumulative length of contigs, and N50 metrics. Ortholog hit ratios (OHR=length of alignment:length of proteins from a close relative) were calculated to estimate assembly fragmentation. Each clustered assembly had a high N50 (2.6-3.2 kb) and a high percentage of hits with an OHR >= 0.8 (52-65%) suggesting the workflow produced high quality assemblies.For de novo transcriptomes, there are few standalone programs that summarize aligned read counts for input into EdgeR or DeSeq. Although popular, HTSeq allows partial length single-end alignments but does not allow alignment of one out of two mates. We developed the custom script Count_reads_denovo.pl, for de novo RNA-Seq projects. Count_reads_denovo.pl uses a model similar to featureCounts, a reference-based summarizer, to leverage paired-end data even where only one mate aligns. The script filters read counts by mapping quality (MAPQ). Scripts used in the above workflow, as well as Count_reads_denovo.pl are available at https://github.com/i5K-KINBRE-script-share/.</p
