Whole Genome Sequencing Variant Call Files for Trypanosoma brucei clones resistant to nifurtimox or fexinidazole

Genomic DNA prepared from parental and drug resistant clones from T.brucei was sequenced on an Illumina HiSeq 2000 sequencer. The Illumina data were aligned against the T.brucei TREU927 reference genome and variants were called using SAMtools v0.1.19 and BCF tools. The files are in variant call format listing all 190064 genomic positions at which any one of the six drug-resistant and the parental parasite lines had a variant call

The genome sequence of rosebay willowherb Chamaenerion angustifolium (L.) Scop., 1771 (syn. Epilobium angustifolium L., 1753) (Onagraceae).

We present a genome assembly from an individual Chamaenerion angustifolium (fireweed; Tracheophyta; Magnoliopsida; Myrtales; Onagraceae). The genome sequence is 655.9 megabases in span. Most of the assembly is scaffolded into 18 chromosomal pseudomolecules. The mitochondrial and plastid genome assemblies have lengths of 495.18 kilobases and 160.41 kilobases in length, respectively

Modelling the Effects of Mass Drug Administration on the Molecular Epidemiology of Schistosomes

As national governments scale up mass drug administration (MDA) programs aimed to combat neglected tropical diseases (NTDs), novel selection pressures on these parasites increase. To understand how parasite populations are affected by MDA and how to maximize the success of control programmes, it is imperative for epidemiological, molecular and mathematical modelling approaches to be combined. Modelling of parasite population genetic and genomic structure, particularly of the NTDs, has been limited through the availability of only a few molecular markers to date. The landscape of infectious disease research is being dramatically reshaped by next-generation sequencing technologies and our understanding of how repeated selective pressures are shaping parasite populations is radically altering. Genomics can provide high-resolution data on parasite population structure, and identify how loci may contribute to key phenotypes such as virulence and/or drug resistance. We discuss the incorporation of genetic and genomic data, focussing on the recently sequenced Schistosoma spp., into novel mathematical transmission models to inform our understanding of the impact of MDA and other control methods. We summarize what is known to date, the models that exist and how population genetics has given us an understanding of the effects of MDA on the parasites. We consider how genetic and genomic data have the potential to shape future research, highlighting key areas where data are lacking, and how future molecular epidemiology knowledge can aid understanding of transmission dynamics and the effects of MDA, ultimately informing public health policy makers of the best interventions for NTDs

The genome sequence of the orange-tip butterfly, <i>Anthocharis cardamines</i> (Linnaeus, 1758).

We present a genome assembly from an individual female Anthocharis cardamines (the orange-tip; Arthropoda; Insecta; Lepidoptera; Pieridae). The genome sequence is 360 megabases in span. The majority (99.74%) of the assembly is scaffolded into 31 chromosomal pseudomolecules, with the W and Z sex chromosomes assembled. Gene annotation of this assembly on Ensembl has identified 12,477 protein coding genes

A chromosomal reference genome sequence for the malaria mosquito Anopheles gambiae, Giles, 1902, Ifakara strain

We present a genome assembly from an individual female Anopheles gambiae (the malaria mosquito; Arthropoda; Insecta; Diptera; Culicidae), Ifakara strain. The genome sequence is 264 megabases in span. Most of the assembly is scaffolded into three chromosomal pseudomolecules with the X sex chromosome assembled. The complete mitochondrial genome was also assembled and is 15.4 kilobases in length

Lineage replacement and evolution captured by 3 years of the United Kingdom Coronavirus (COVID-19) Infection Survey

The Office for National Statistics Coronavirus (COVID-19) Infection Survey (ONS-CIS) is the largest surveillance study of SARS-CoV-2 positivity in the community, and collected data on the United Kingdom (UK) epidemic from April 2020 until March 2023 before being paused. Here, we report on the epidemiological and evolutionary dynamics of SARS-CoV-2 determined by analysing the sequenced samples collected by the ONS-CIS during this period. We observed a series of sweeps or partial sweeps, with each sweeping lineage having a distinct growth advantage compared to their predecessors, although this was also accompanied by a gradual fall in average viral burdens from June 2021 to March 2023. The sweeps also generated an alternating pattern in which most samples had either S-gene target failure (SGTF) or non-SGTF over time. Evolution was characterized by steadily increasing divergence and diversity within lineages, but with step increases in divergence associated with each sweeping major lineage. This led to a faster overall rate of evolution when measured at the between-lineage level compared to within lineages, and fluctuating levels of diversity. These observations highlight the value of viral sequencing integrated into community surveillance studies to monitor the viral epidemiology and evolution of SARS-CoV-2, and potentially other pathogens

Multiplicity: an organizing principle for cancers and somatic mutations

<p>Abstract</p> <p>Background</p> <p>With the advent of whole-genome analysis for profiling tumor tissue, a pressing need has emerged for principled methods of organizing the large amounts of resulting genomic information. We propose the concept of multiplicity measures on cancer and gene networks to organize the information in a clinically meaningful manner. Multiplicity applied in this context extends Fearon and Vogelstein's multi-hit genetic model of colorectal carcinoma across multiple cancers.</p> <p>Methods</p> <p>Using the Catalogue of Somatic Mutations in Cancer (COSMIC), we construct networks of interacting cancers and genes. Multiplicity is calculated by evaluating the number of cancers and genes linked by the measurement of a somatic mutation. The Kamada-Kawai algorithm is used to find a two-dimensional minimum energy solution with multiplicity as an input similarity measure. Cancers and genes are positioned in two dimensions according to this similarity. A third dimension is added to the network by assigning a maximal multiplicity to each cancer or gene. Hierarchical clustering within this three-dimensional network is used to identify similar clusters in somatic mutation patterns across cancer types.</p> <p>Results</p> <p>The clustering of genes in a three-dimensional network reveals a similarity in acquired mutations across different cancer types. Surprisingly, the clusters separate known causal mutations. The multiplicity clustering technique identifies a set of causal genes with an area under the ROC curve of 0.84 versus 0.57 when clustering on gene mutation rate alone. The cluster multiplicity value and number of causal genes are positively correlated via Spearman's Rank Order correlation (<it>r_s</it>(8) = 0.894, Spearman's <it>t </it>= 17.48, <it>p </it>< 0.05). A clustering analysis of cancer types segregates different types of cancer. All blood tumors cluster together, and the cluster multiplicity values differ significantly (Kruskal-Wallis, <it>H </it>= 16.98, <it>df </it>= 2, <it>p </it>< 0.05).</p> <p>Conclusion</p> <p>We demonstrate the principle of multiplicity for organizing somatic mutations and cancers in clinically relevant clusters. These clusters of cancers and mutations provide representations that identify segregations of cancer and genes driving cancer progression.</p

Aquatic Symbiosis Genomics Project leadership

&lt;p&gt;This document holds a record of the team leads at the Wellcome Sanger Institute and genome collection hubs who should be considered coauthors of publications arising from the Aquatic Symbiosis Genomics project (&lt;a href="https://www.aquaticsymbiosisgenomics.org/"&gt;https://www.aquaticsymbiosisgenomics.org/&lt;/a&gt;).&nbsp;&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt