Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Multiplatform Analysis of 12 Cancer Types Reveals Molecular Classification within and across Tissues of Origin

Recent genomic analyses of pathologically-defined tumor types identify “within-a-tissue” disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head & neck, and a subset of bladder cancers coalesced into one subtype typified by TP53 alterations, TP63 amplifications, and high expression of immune and proliferation pathway genes. Of note, bladder cancers split into three pan-cancer subtypes. The multi-platform classification, while correlated with tissue-of-origin, provides independent information for predicting clinical outcomes. All datasets are available for data-mining from a unified resource to support further biological discoveries and insights into novel therapeutic strategies

Predicting Mortality among Hospitalized Children with Respiratory Illness in Western Kenya, 2009–2012

<div><p>Background</p><p>Pediatric respiratory disease is a major cause of morbidity and mortality in the developing world. We evaluated a modified respiratory index of severity in children (mRISC) scoring system as a standard tool to identify children at greater risk of death from respiratory illness in Kenya.</p><p>Materials and Methods</p><p>We analyzed data from children <5 years old who were hospitalized with respiratory illness at Siaya District Hospital from 2009–2012. We used a multivariable logistic regression model to identify patient characteristics predictive for in-hospital mortality. Model discrimination was evaluated using the concordance statistic. Using bootstrap samples, we re-estimated the coefficients and the optimism of the model. The mRISC score for each child was developed by adding up the points assigned to each factor associated with mortality based on the coefficients in the multivariable model.</p><p>Results</p><p>We analyzed data from 3,581 children hospitalized with respiratory illness; including 218 (6%) who died. Low weight-for-age [adjusted odds ratio (aOR) = 2.1; 95% CI 1.3–3.2], very low weight-for-age (aOR = 3.8; 95% CI 2.7–5.4), caretaker-reported history of unconsciousness (aOR = 2.3; 95% CI 1.6–3.4), inability to drink or breastfeed (aOR = 1.8; 95% CI 1.2–2.8), chest wall in-drawing (aOR = 2.2; 95% CI 1.5–3.1), and being not fully conscious on physical exam (aOR = 8.0; 95% CI 5.1–12.6) were independently associated with mortality. The positive predictive value for mortality increased with increasing mRISC scores.</p><p>Conclusions</p><p>A modified RISC scoring system based on a set of easily measurable clinical features at admission was able to identify children at greater risk of death from respiratory illness in Kenya.</p></div

Bivariate associations between demographic characteristics, hospital admission clinical signs and clinical symptoms and mortality among children <5 years hospitalized with a respiratory illness in Siaya District Hospital, August 2009–July 2012.

a<p>Defined as per the WHO IMCI case definition: Cough or difficult breathing and elevated respiratory rate;</p>b<p>Defined as per the WHO IMCI case definition: Cough or difficult breathing and any general danger sign or chest in-drawing or stridor in calm child;</p>c<p>Elevated respiratory rate for age based on WHO IMCI algorithm; <2 months, >60 breaths/minute; 2–11 months, >50 breaths/minute;12–59 months, >40 breaths/minute;</p>d<p>Combines responds to voice commands, responds to mild pain and unresponsive/unconscious;</p><p>*All admissions including deaths;</p>Ŧ<p>Enrolled in research studies conducted by KEMRI/CDC (Malaria and TB studies);</p>¥<p>Variables that were not statistically significant at α = 0.05 are not shown on the table.</p

Children included in, and excluded from, the evaluation of mRISC score.

<p>Children included in, and excluded from, the evaluation of mRISC score.</p

Modified Respiratory Index of Severity in Children (mRISC) scoring.

<p>Modified Respiratory Index of Severity in Children (mRISC) scoring.</p

Receiver operating curve (ROC) for the scoring model.

<p>Receiver operating curve (ROC) for the scoring model.</p

Observed mortality (with 95% CI) and mean predicted mortality by severity score.

<p>Observed mortality (with 95% CI) and mean predicted mortality by severity score.</p

Factors associated with mortality among children <5 years hospitalized with a respiratory illness in Siaya District Hospital, August 2009–July 2012.

a<p>Interaction between malaria and chest wall in-drawing;</p>b<p>Combines responds to voice commands, responds to mild pain and unresponsive/unconscious;</p>Ŧ<p>Adjusted for enrollment in clinical research studies, treatment.</p