It's unlikely that the underlying aligner would have run at different speeds on different systems since they're both going to be calling out to the same code. It seems more likely that the aligners were run with different parameters (different levels of multi-threading, different alignment stringencies etc) which can have a big effect on how long they take to process their data.
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Strand NGS, the integrated analysis platform
Strand NGS (Formerly Avadis NGS) is an integrated platform that provides analysis, management and visualization tools for next-generation sequencing data. It supports extensive workflows for alignment, RNA-Seq, small RNA-Seq, DNA-Seq, Methyl-Seq, MeDIP-Seq, and ChIP-Seq experiments.
Pipeline manager option can be used to streamline large scale analysis.
More details on various enhancements/features can be found at http://www.strand-ngs.com/features
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Strand NGS has improved a lot. The user interface is similar to that of Agilent Technologies GeneSpring, since it is built on same platform Avadis. Strand NGS is very flexible, since its interface auto-adopts to experiment type, only showing relevant analysis steps and functions (i.e guided workflows). You can also customize via R scripts and Jython scripts. Annotation Manager provides access to annotations from within the tool and many more
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Despite advancements in sequencing platforms and related sample preparation technologies, certain sample types continue to present significant challenges that can compromise sequencing results. Pedro Echave, Senior Manager of the Global Business Segment at Revvity, explained that the success of a sequencing experiment ultimately depends on the amount and integrity of the nucleic acid template (RNA or DNA) obtained from a sample. “The better the quality of the nucleic acid isolated...-
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The field of conservation genomics centers on applying genomics technologies in support of conservation efforts and the preservation of biodiversity. This article features interviews with two researchers who showcase their innovative work and highlight the current state and future of conservation genomics.
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