Addressing challenges in the production and analysis of illumina sequencing data

Advances in DNA sequencing technologies have made it possible to generate large amounts of sequence data very rapidly and at substantially lower cost than capillary sequencing. These new technologies have specific characteristics and limitations that require either consideration during project design, or which must be addressed during data analysis. Specialist skills, both at the laboratory and the computational stages of project design and analysis, are crucial to the generation of high quality data from these new platforms. The Illumina sequencers (including the Genome Analyzers I/II/IIe/IIx and the new HiScan and HiSeq) represent a widely used platform providing parallel readout of several hundred million immobilized sequences using fluorescent-dye reversible-terminator chemistry. Sequencing library quality, sample handling, instrument settings and sequencing chemistry have a strong impact on sequencing run quality. The presence of adapter chimeras and adapter sequences at the end of short-insert molecules, as well as increased error rates and short read lengths complicate many computational analyses. We discuss here some of the factors that influence the frequency and severity of these problems and provide solutions for circumventing these. Further, we present a set of general principles for good analysis practice that enable problems with sequencing runs to be identified and dealt with

Structural basis for the photoconversion of a phytochrome to the activated far-red light-absorbing form

Phytochromes are a collection of bilin-containing photoreceptors that regulate numerous photoresponses in plants and microorganisms through their ability to photointerconvert between a red light-absorbing, ground state Pr and a far-red light-absorbing, photoactivated state Pfr1,2. While the structures of several phytochromes as Pr have been determined3-7, little is known about the structure of Pfr and how it initiates signaling. Here, we describe the three-dimensional solution structure of the bilin-binding domain as Pfr using the cyanobacterial phytochrome from Synechococcus OSB’. Contrary to predictions, light-induced rotation of the A but not the D pyrrole ring is the primary motion of the chromophore during photoconversion. Subsequent rearrangements within the protein then affect intra- and interdomain contact sites within the phytochrome dimer. From our models, we propose that phytochromes act by propagating reversible light-driven conformational changes in the bilin to altered contacts between the adjacent output domains, which in most phytochromes direct differential phosphotransfer

The Genome of a Pathogenic Rhodococcus: Cooptive Virulence Underpinned by Key Gene Acquisitions

We report the genome of the facultative intracellular parasite Rhodococcus equi, the only animal pathogen within the biotechnologically important actinobacterial genus Rhodococcus. The 5.0-Mb R. equi 103S genome is significantly smaller than those of environmental rhodococci. This is due to genome expansion in nonpathogenic species, via a linear gain of paralogous genes and an accelerated genetic flux, rather than reductive evolution in R. equi. The 103S genome lacks the extensive catabolic and secondary metabolic complement of environmental rhodococci, and it displays unique adaptations for host colonization and competition in the short-chain fatty acid–rich intestine and manure of herbivores—two main R. equi reservoirs. Except for a few horizontally acquired (HGT) pathogenicity loci, including a cytoadhesive pilus determinant (rpl) and the virulence plasmid vap pathogenicity island (PAI) required for intramacrophage survival, most of the potential virulence-associated genes identified in R. equi are conserved in environmental rhodococci or have homologs in nonpathogenic Actinobacteria. This suggests a mechanism of virulence evolution based on the cooption of existing core actinobacterial traits, triggered by key host niche–adaptive HGT events. We tested this hypothesis by investigating R. equi virulence plasmid-chromosome crosstalk, by global transcription profiling and expression network analysis. Two chromosomal genes conserved in environmental rhodococci, encoding putative chorismate mutase and anthranilate synthase enzymes involved in aromatic amino acid biosynthesis, were strongly coregulated with vap PAI virulence genes and required for optimal proliferation in macrophages. The regulatory integration of chromosomal metabolic genes under the control of the HGT–acquired plasmid PAI is thus an important element in the cooptive virulence of R. equi

Genomic reconstruction of the SARS-CoV-2 epidemic in England.

The evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus leads to new variants that warrant timely epidemiological characterization. Here we use the dense genomic surveillance data generated by the COVID-19 Genomics UK Consortium to reconstruct the dynamics of 71 different lineages in each of 315 English local authorities between September 2020 and June 2021. This analysis reveals a series of subepidemics that peaked in early autumn 2020, followed by a jump in transmissibility of the B.1.1.7/Alpha lineage. The Alpha variant grew when other lineages declined during the second national lockdown and regionally tiered restrictions between November and December 2020. A third more stringent national lockdown suppressed the Alpha variant and eliminated nearly all other lineages in early 2021. Yet a series of variants (most of which contained the spike E484K mutation) defied these trends and persisted at moderately increasing proportions. However, by accounting for sustained introductions, we found that the transmissibility of these variants is unlikely to have exceeded the transmissibility of the Alpha variant. Finally, B.1.617.2/Delta was repeatedly introduced in England and grew rapidly in early summer 2021, constituting approximately 98% of sampled SARS-CoV-2 genomes on 26 June 2021

A global reference for human genetic variation

The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.We thank the many people who were generous with contributing their samples to the project: the African Caribbean in Barbados; Bengali in Bangladesh; British in England and Scotland; Chinese Dai in Xishuangbanna, China; Colombians in Medellin, Colombia; Esan in Nigeria; Finnish in Finland; Gambian in Western Division – Mandinka; Gujarati Indians in Houston, Texas, USA; Han Chinese in Beijing, China; Iberian populations in Spain; Indian Telugu in the UK; Japanese in Tokyo, Japan; Kinh in Ho Chi Minh City, Vietnam; Luhya in Webuye, Kenya; Mende in Sierra Leone; people with African ancestry in the southwest USA; people with Mexican ancestry in Los Angeles, California, USA; Peruvians in Lima, Peru; Puerto Ricans in Puerto Rico; Punjabi in Lahore, Pakistan; southern Han Chinese; Sri Lankan Tamil in the UK; Toscani in Italia; Utah residents (CEPH) with northern and western European ancestry; and Yoruba in Ibadan, Nigeria. Many thanks to the people who contributed to this project: P. Maul, T. Maul, and C. Foster; Z. Chong, X. Fan, W. Zhou, and T. Chen; N. Sengamalay, S. Ott, L. Sadzewicz, J. Liu, and L. Tallon; L. Merson; O. Folarin, D. Asogun, O. Ikpwonmosa, E. Philomena, G. Akpede, S. Okhobgenin, and O. Omoniwa; the staff of the Institute of Lassa Fever Research and Control (ILFRC), Irrua Specialist Teaching Hospital, Irrua, Edo State, Nigeria; A. Schlattl and T. Zichner; S. Lewis, E. Appelbaum, and L. Fulton; A. Yurovsky and I. Padioleau; N. Kaelin and F. Laplace; E. Drury and H. Arbery; A. Naranjo, M. Victoria Parra, and C. Duque; S. Däkel, B. Lenz, and S. Schrinner; S. Bumpstead; and C. Fletcher-Hoppe. Funding for this work was from the Wellcome Trust Core Award 090532/Z/09/Z and Senior Investigator Award 095552/Z/11/Z (P.D.), and grants WT098051 (R.D.), WT095908 and WT109497 (P.F.), WT086084/Z/08/Z and WT100956/Z/13/Z (G.M.), WT097307 (W.K.), WT0855322/Z/08/Z (R.L.), WT090770/Z/09/Z (D.K.), the Wellcome Trust Major Overseas program in Vietnam grant 089276/Z.09/Z (S.D.), the Medical Research Council UK grant G0801823 (J.L.M.), the UK Biotechnology and Biological Sciences Research Council grants BB/I02593X/1 (G.M.) and BB/I021213/1 (A.R.L.), the British Heart Foundation (C.A.A.), the Monument Trust (J.H.), the European Molecular Biology Laboratory (P.F.), the European Research Council grant 617306 (J.L.M.), the Chinese 863 Program 2012AA02A201, the National Basic Research program of China 973 program no. 2011CB809201, 2011CB809202 and 2011CB809203, Natural Science Foundation of China 31161130357, the Shenzhen Municipal Government of China grant ZYC201105170397A (J.W.), the Canadian Institutes of Health Research Operating grant 136855 and Canada Research Chair (S.G.), Banting Postdoctoral Fellowship from the Canadian Institutes of Health Research (M.K.D.), a Le Fonds de Recherche duQuébec-Santé (FRQS) research fellowship (A.H.), Genome Quebec (P.A.), the Ontario Ministry of Research and Innovation – Ontario Institute for Cancer Research Investigator Award (P.A., J.S.), the Quebec Ministry of Economic Development, Innovation, and Exports grant PSR-SIIRI-195 (P.A.), the German Federal Ministry of Education and Research (BMBF) grants 0315428A and 01GS08201 (R.H.), the Max Planck Society (H.L., G.M., R.S.), BMBF-EPITREAT grant 0316190A (R.H., M.L.), the German Research Foundation (Deutsche Forschungsgemeinschaft) Emmy Noether Grant KO4037/1-1 (J.O.K.), the Beatriu de Pinos Program grants 2006 BP-A 10144 and 2009 BP-B 00274 (M.V.), the Spanish National Institute for Health Research grant PRB2 IPT13/0001-ISCIII-SGEFI/FEDER (A.O.), Ewha Womans University (C.L.), the Japan Society for the Promotion of Science Fellowship number PE13075 (N.P.), the Louis Jeantet Foundation (E.T.D.), the Marie Curie Actions Career Integration grant 303772 (C.A.), the Swiss National Science Foundation 31003A_130342 and NCCR “Frontiers in Genetics” (E.T.D.), the University of Geneva (E.T.D., T.L., G.M.), the US National Institutes of Health National Center for Biotechnology Information (S.S.) and grants U54HG3067 (E.S.L.), U54HG3273 and U01HG5211 (R.A.G.), U54HG3079 (R.K.W., E.R.M.), R01HG2898 (S.E.D.), R01HG2385 (E.E.E.), RC2HG5552 and U01HG6513 (G.T.M., G.R.A.), U01HG5214 (A.C.), U01HG5715 (C.D.B.), U01HG5718 (M.G.), U01HG5728 (Y.X.F.), U41HG7635 (R.K.W., E.E.E., P.H.S.), U41HG7497 (C.L., M.A.B., K.C., L.D., E.E.E., M.G., J.O.K., G.T.M., S.A.M., R.E.M., J.L.S., K.Y.), R01HG4960 and R01HG5701 (B.L.B.), R01HG5214 (G.A.), R01HG6855 (S.M.), R01HG7068 (R.E.M.), R01HG7644 (R.D.H.), DP2OD6514 (P.S.), DP5OD9154 (J.K.), R01CA166661 (S.E.D.), R01CA172652 (K.C.), P01GM99568 (S.R.B.), R01GM59290 (L.B.J., M.A.B.), R01GM104390 (L.B.J., M.Y.Y.), T32GM7790 (C.D.B., A.R.M.), P01GM99568 (S.R.B.), R01HL87699 and R01HL104608 (K.C.B.), T32HL94284 (J.L.R.F.), and contracts HHSN268201100040C (A.M.R.) and HHSN272201000025C (P.S.), Harvard Medical School Eleanor and Miles Shore Fellowship (K.L.), Lundbeck Foundation Grant R170-2014-1039 (K.L.), NIJ Grant 2014-DN-BX-K089 (Y.E.), the Mary Beryl Patch Turnbull Scholar Program (K.C.B.), NSF Graduate Research Fellowship DGE-1147470 (G.D.P.), the Simons Foundation SFARI award SF51 (M.W.), and a Sloan Foundation Fellowship (R.D.H.). E.E.E. is an investigator of the Howard Hughes Medical Institute

Baroreflex control of coronary blood flow varies regionally in awake dogs

1. Baroreflex responses to changes in aortic pressure were measured simulataneously in three main coronary regions of awake dogs. 2. Pulsed Doppler flow probes were mounted at prior surgery on the right, circumflex and anterior descending coronary arteries; the animals were placed in complete heart block and the left ventricle was placed. After 2-4 weeks recovery, baroreflexes were evoked by inflating a balloon catheter placed in the mid-thoracic aorta via the femoral arteriotomy. Flow and pressure data were collected at rest, and during acute (8 s) and steady-state (25 s) baroreflex challenge. 3. Changing ventricular rate alone caused a fall in aortic pressure at low rates; however, over the range 60 to 180 b.p.m., circumflex and anterior descending coronary flow and conductance changed directly with ventricular rate, but right coronary flow and conductance remained unchanged. 4. Acute aortic pressure elevation increased flow at 8 s in all beds at all rates. Conductance effects at 60 b.p.m. were negligible in all three beds, but rose at 100 and 180 b.p.m. in the right and circumflex beds. 5. Sustained aortic pressure elevation (25 s) caused flow to return towards control in all beds at all ventricular rates, but in the right coronary at 60 b.p.m. flow fell below control. Conductance at this time was unchanged at all rates in the anterior descending bed, fell modestly in the circumflex, and decreased to below resting in the right coronary bed. 6. Baroreflex control of coronary flow and conductance thus varies between territories, and within territories, depending on ventricular rate. The right coronary bed appears to be regulated by a bidirectional, baroreflex-linked mechanism, which is functionally opposite in action to that found in most vascular bed