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We have attempted to minimise the impact of platform versions by using the same pipelines and methods for alignment and variant calling.> The crowdsourced data encompass genotypes, annotations, alignments, whole exome sequencing and metagenomics sequencing.
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We submitted five runs for the CEM subtask, each using the same pipeline, but with different values for the cost parameter in the CRF model [12, 14].
In our post-challenge improved system, we reobtained five runs for the CEM subtask, each using the same pipeline as official submissions, but with different features sets (Table 5).
The data in [15] were analyzed using the same pipeline described above for our data; binding sites were identified using an adjusted p-value of 10−4.
The sub gene-families were then analyzed using the same pipeline as used for synthetic analysis.
The CS distributions were obtained for the yeast proteome using the same pipeline.
For the H6 and SP3B assemblies, we performed gene prediction using the same pipeline (Table 1).
All reads were processed using the same pipeline as for all biological RNA-seq data.
Subsets of reads, with depths at 6, 12, 24, 48, and 120 Mmr, were analyzed using the same pipeline.
First, reads were processed to identify miRNA alignments using the same pipeline that we use for Small-RNA-Seq listed above.
The 100bp PET reads of the RILs were mapped to the reference sequence and SNPs called using the same pipeline and criteria.
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