Symbolic representation of classification profiles mixture.

<p>Here the mixture components indicate the fraction of all hits mapping against loci used to build a profile. Transition frequencies across introns match the Illumina coverage density at each particular splice site.</p

Transcriptome model.

<p>Here are the prior probabilities of cattle being infected based on previous experience. Each profiles mixture has structure as shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050147#pone-0050147-g004" target="_blank">Figure 4</a>.</p

Gene expression changes.

<p>Gene expression changes.</p

Number of reads.

<p>Number of reads from different pooled transcriptome samples and the total number of reads mapped against the Btau 4.0 reference genome.</p

Number of mapped reads.

<p>Number of reads mapped against informative loci.</p

Example of IFN- and IL17A unnormalized gene expression changes in response to bTB.

<p>On these GBrowse views we show coverage for mapped RNA-Seq reads along with Illumina short reads spanning across introns, i.e. cDNA reads that that partially map to two different exons thus anchoring the exonic boundaries. Here the control reads are the reads from TCT1 pool.</p

Probabilities of emissions that happen after each transition are shown in bold and transitions of interest taken at certain time-point are underlined

<p><b>Copyright information:</b></p><p>Taken from "Implementing EM and Viterbi algorithms for Hidden Markov Model in linear memory"</p><p>http://www.biomedcentral.com/1471-2105/9/224</p><p>BMC Bioinformatics 2008;9():224-224.</p><p>Published online 30 Apr 2008</p><p>PMCID:PMC2430973.</p><p></p

In subfigures 8(d) – 8(f) performance of Baum-Welch algorithm used on spike topology for various ionic flow durations is shown

<p><b>Copyright information:</b></p><p>Taken from "Implementing EM and Viterbi algorithms for Hidden Markov Model in linear memory"</p><p>http://www.biomedcentral.com/1471-2105/9/224</p><p>BMC Bioinformatics 2008;9():224-224.</p><p>Published online 30 Apr 2008</p><p>PMCID:PMC2430973.</p><p></p

Mixture of convolutions for Aggregate states 1 (Agr1) and 4 (Agr4) where in brackets we include mixture component number

<p><b>Copyright information:</b></p><p>Taken from "Duration learning for analysis of nanopore ionic current blockades"</p><p>http://www.biomedcentral.com/1471-2105/8/S7/S14</p><p>BMC Bioinformatics 2007;8(Suppl 7):S14-S14.</p><p>Published online 1 Nov 2007</p><p>PMCID:PMC2099482.</p><p></p> Transitions with weight 0.001 are negligible and were forcefully assigned by learning algorithms not to cause underflow in forward-backward procedure

Recovered duration histograms by learning the randomly initialized explicit duration DHMM for the maximum state duration of 30

<p><b>Copyright information:</b></p><p>Taken from "Duration learning for analysis of nanopore ionic current blockades"</p><p>http://www.biomedcentral.com/1471-2105/8/S7/S14</p><p>BMC Bioinformatics 2007;8(Suppl 7):S14-S14.</p><p>Published online 1 Nov 2007</p><p>PMCID:PMC2099482.</p><p></p