12 Matching Annotations
  1. Jul 2015
    1. lookback

      Previous line

    2. current

      Current working line on the alignment matrix

    3. nw_indel

      (Negative) How much score you get for an insertion or deletion

    4. nw_match

      (Positive) How much score you get for a match

  2. Jun 2015
    1. Note: I'm unconvinced of Promise.race's usefulness; I'd rather have an opposite of Promise.all that only rejects if all items reject.

      This is hurting my brain. Can we just use a meta-promise that fulfills when rejected, and reject when fulfilled in order to achieve that?

    1. The MinION nanopore sequencer can produce long sequencing reads on a device similar in size to a USB memory stick.

      Fantastic

    Annotators

    1. Formal languages and compiler

      One can always dream...

    Annotators

    URL

    1. a public channel

      This is intentionally vague because we aren't sure how to do this yet.

    2. community effort

      Community = Algorithm (Suggestion of Facts) Scientists (Access + Initial Annotation of Facts) + Ordinary people (Refinements)

    3. an open-source license

      PDF.js = Apache AnnotatorJS = MIT or GPLv3

      We are unsure what to choose for the license yet.

    1. A more serious concern is that if one happens to let the battery fully depleted, the machine forgets that it is in the developer mode. It then would refuse to boot the custom OS, and force the user to delete all the data in the SSD while restoring the original operating system.

      I realize that this is not really a problem if you set the GBB flags correctly, because the GBB doesn't get erased when the battery dies.

  3. May 2015
    1. We surveyed the ruminal metagenomes of 16 sheep under two different diets using Illumina pair-end DNA sequencing of raw microbial DNA extracted from rumen samples. The resulting sequence data were bioinformatically mapped to known prokaryotic 16S rDNA sequences to identify the taxa present in the samples and then analysed for the presence of potentially new taxa. Strikingly, the majority of the microbial individuals found did not map to known taxa from 16S sequence databases. We used a novel statistical modelling approach to compare the taxonomic distributions between animals fed a forage-based diet and those fed concentrated grains. With this model, we found significant differences between the two groups both in the dominant taxa present in the rumen and in the overall shape of the taxa abundance curves. In general, forage-fed animals have a more diverse microbial ecosystem, whereas the concentrate-fed animals have ruminal systems more heavily dominated by a few taxa. As expected, organisms from methanogenic groups are more prevalent in forage-fed animals. Finally, all of these differences appear to be grounded in an underlying common input of new microbial individuals into the rumen environment, with common organisms from one feed group being present in the other, but at much lower abundance.

      In general, forage-fed animals have a more diverse microbial ecosystem, whereas the concentrate-fed animals have ruminal systems more heavily dominated by a few taxa.