Mitochondrial Genomic Landscape: A Portrait of the Mitochondrial Genome 40 Years after the First Complete Sequence

Notwithstanding the initial claims of general conservation, mitochondrial genomes are a largely heterogeneous set of organellar chromosomes which displays a bewildering diversity in terms of structure, architecture, gene content, and functionality. The mitochondrial genome is typically described as a single chromosome, yet many examples of multipartite genomes have been found (for example, among sponges and diplonemeans); the mitochondrial genome is typically depicted as circular, yet many linear genomes are known (for example, among jellyfish, alveolates, and apicomplexans); the chromosome is normally said to be “small”, yet there is a huge variation between the smallest and the largest known genomes (found, for example, in ctenophores and vascular plants, respectively); even the gene content is highly unconserved, ranging from the 13 oxidative phosphorylation-related enzymatic subunits encoded by animal mitochondria to the wider set of mitochondrial genes found in jakobids. In the present paper, we compile and describe a large database of 27,873 mitochondrial genomes currently available in GenBank, encompassing the whole eukaryotic domain. We discuss the major features of mitochondrial molecular diversity, with special reference to nucleotide composition and compositional biases; moreover, the database is made publicly available for future analyses on the MoZoo Lab GitHub page.

Complete Genome Sequences of 10 Lactococcal Skunavirus Phages Isolated from Cheddar Cheese Whey Samples in Canada

ABSTRACT We report the complete genome sequences of 10 virulent phages of the Skunavirus genus (Siphoviridae) that infect Lactococcus lactis strains used for cheddar cheese production in Canada. Their linear genomes range from 28,969 bp to 31,042 bp with GC contents of 34.1 to 35.1% and 55 to 60 predicted open reading frames (ORFs).

Longest Order Conserved Exemplar Subsequences

We propose a new problem whose input data are two linear genomes together with two indexed gene subsequences of them, which asks to find a longest common exemplar subsequence of the two given genomes with a subsequence identical to the given indexed gene subsequences. We present an algorithm for this problem such that the algorithm is allowed to take diminishing time and space to solve the problem by setting the indexed genes with an incremental number. Although an incremental number of indexed genes were selected, the algorithm was verified definite to reach a solution whose length insistently comes very close to a real longest common exemplar subsequence of the two given genomes. Aiming at 23 human/gorilla chromosome pairs, the algorithm was examined for use in questing for longest common exemplar subsequences whose basic units are annotated genes as well as pseudo genes, namely consecutive DNA subsequences. By contrasting the pseudo gene common exemplar subsequences the algorithm had reached for the human chromosomes 7 and 16 and their gorilla homologues with those annotated genes in the human and gorilla chromosomes, we found more than 1000 and 500 pseudo genes in the human chromosomes 7 and 16 that occur in the same order as they are in the gorilla chromosomes 7 and 16 and, do not overlap with any annotated gene.

Distance indexing and seed clustering in sequence graphs

Motivation Graph representations of genomes are capable of expressing more genetic variation and can therefore better represent a population than standard linear genomes. However, due to the greater complexity of genome graphs relative to linear genomes, some functions that are trivial on linear genomes become much more difficult in genome graphs. Calculating distance is one such function that is simple in a linear genome but complicated in a graph context. In read mapping algorithms such distance calculations are fundamental to determining if seed alignments could belong to the same mapping. Results We have developed an algorithm for quickly calculating the minimum distance between positions on a sequence graph using a minimum distance index. We have also developed an algorithm that uses the distance index to cluster seeds on a graph. We demonstrate that our implementations of these algorithms are efficient and practical to use for a new generation of mapping algorithms based upon genome graphs. Availability and implementation Our algorithms have been implemented as part of the vg toolkit and are available at https://github.com/vgteam/vg.

Distance Indexing and Seed Clustering in Sequence Graphs

Graph representations of genomes are capable of expressing more genetic variation and can therefore better represent a population than standard linear genomes. However, due to the greater complexity of genome graphs relative to linear genomes, some functions that are trivial on linear genomes become more difficult in genome graphs. Calculating distance is one such function that is simple in a linear genome but much more complicated in a graph context. In read mapping algorithms, distance calculations are commonly used in a clustering step to determine if seed alignments could belong to the same mapping. Clustering algorithms are a bottleneck for some mapping algorithms due to the cost of repeated distance calculations. We have developed an algorithm for quickly calculating the minimum distance between positions on a sequence graph using a minimum distance index. We have also developed an algorithm that uses the distance index to cluster seeds on a graph. We demonstrate that our implementations of these algorithms are efficient and practical to use for mapping algorithms.

karyoploteR: an R/Bioconductor package to plot customizable linear genomes displaying arbitrary data

MotivationData visualization is a crucial tool for data exploration, analysis and interpretation. For the visualization of genomic data there lacks a tool to create customizable non-circular plots of whole genomes from any species.ResultsWe have developed karyoploteR, an R/Bioconductor package to create linear chromosomal representations of any genome with genomic annotations and experimental data plotted along them. Plot creation process is inspired in R base graphics, with a main function creating karyoplots with no data and multiple additional functions, including custom functions written by the end-user, adding data and other graphical elements. This approach allows the creation of highly customizable plots from arbitrary data with complete freedom on data positioning and representation.AvailabilitykaryoploteR is released under Artistic-2.0 License. Source code and documentation are freely available through Bioconductor (http://www.bioconductor.org/packages/karyoploteR)[email protected]