Come see NCBI at the ASM Microbe Conference 2022

The American Society of Microbiology (ASM) Microbe conference is back, and scheduled to take place in-person, June 9th-13th in Washington, D.C. NCBI staff member Dr. Michael Feldgarden will be recognized by ASM with an award for his research. Other NCBI staff will present posters on NCBI resources and will also be available at our booth … Continue reading Come see NCBI at the ASM Microbe Conference 2022

The post Come see NCBI at the ASM Microbe Conference 2022 appeared first on NCBI Insights.

Karen Miga and the telomere-to-telomere consortium

Karen Miga deserves a lot of the credit for the complete human genome sequence.

Karen Miga is a professor at the University of California, Santa Cruz, and she's been working for several years on sequencing the repetitive regions of the genome. She is a co-founder of the telomere-to-telomere consortium that just published a complete sequece of the human genome. She made a signficant contribution to long-read (~20 Kb) and ultra-long-read (>100 kb) sequencing and that's a major technological achievement that's worthy of prizes.

Read the interview on CBC (Canada) Quirks & Quarks at Scientists sequence complete, gap-free human genome for the first time and watch the YouTube video.

Miga did her Ph.D. with Huntington Willard at Duke University. Hunt has been working on centromeres for more than 40 yeas years and some of my colleagues may remember him when he was a professor at the University of Toronto in the Department of Medical Genetics.

John Mattick presents his view of genomes

John Mattick has a new book coming out in August where he defends the notion that most of our genome is full of genes for functonal noncoding RNAs. We have a pretty good idea what he's going to say. This is a talk he gave at Oxford on May 17, 2019.

Here are a few statements that should pique your interest.

  • (0:57) He says that his upcoming book is tentatively titled "the misunderstandings of molecular biology."
  • (1:11) He says that "the assumption has been very deeply embedded from the time of the lac operon on that genes equated to proteins."
  • (2:30) There have been three "surprises" in molecuular biology: (1) introns, (2) eukaryotic genomes are full of 'selfish' DNA, and (3) "gene number does not scale with developmental complexity."
  • (4:30) It is an unjustified assumption to assume that transposon-related seqences are junk and that leads to misinterpretation of neutral evolution.
  • (6:00) The view that evolution of regulatory sequences is mostly responsible for developmental complexity (Evo-Devo) has never been justified.
  • (8:45) A lot of obtuse theoretical discussion about how the number of regulatory protein-coding genes increases quadratically as the total number of protein-coding genes increase in a bacterial genome but at some point there has to be more protein-coding regulatory genes than total protein-coding genes so that limits the evolution of bacteria.
  • (13:40) The proportion of noncoding DNA increases with developmental complexity, topping out at humans.
  • (14:00) The vast majority of the genome in complex organisms is differentially transcribed in different cells and different tissues.
  • (14:15) The whole genome is alive on both strands.
  • (14:20) There are two possibilities: junk RNA or abundant functional transcripts and that explains complex organisms.
  • Mattick then takes several minutes to document the fact that there are abundant transcripts— a fact that has been known for the better part of sixty years but he does not mention that. All of his statements carry the implicit assumption that these transcripts are functional.
  • (20:20) He makes the boring, and largely irelevant, point that most disease-associated loci are located in noncoding regions (GWAS). He's responding to a critic who asked why, if these things (transcripts) are real, don't we see genetic evidence of it.
  • (24:00) Noncoding RNAs have all of the characteristics of functional RNAs with an emphasis on the fact that their expression is often only detected in specific cell types.
  • (31:50) It has now been shown that everything that protein transcription factors can do can be done by noncoding RNA.
  • (32:15) "I want to say to you that conservation is totally misunderstood." Apparently, lack of conservation imputes nothing about function.
  • (41:00) RNAs control phase separation. There's a whole other level of cell organization that we never dreamed of. (Ironically, he gives nucleoli as an example of something we never dreamed of.)
  • (42:36) "This is called soft metaphysics, and it's just come into biology, and it's spectacular in its implications."
  • (46:25) Almost every lncRNA is alternatively spliced in mice and humans.
  • (46:30) There's more alternative splicing in human protein-coding genes than in mice protein-coding genes but the extra splicing in humans is mostly in the 5' untranslated region. (I'm sure it has nothing to do with the fact that tons more RNA-Seq experiments have been done on human tissues.) "We think this is due to the increased sophistication of the regulation of these genes for the evolution of cognition."
  • (48:00) At least 20% of the human genome is evolutionarily conserved at the level of RNA structure and this does not require any assumptions.
  • (55:00) The talk ends at 55 minutes. That's too bad because I'm sure Mattick had a dozen more slides explaining why all of those transcripts are functional, as opposed to the few selected examples he picked. I'm sure he also had a lot of data refuting all of the evidence in favor of junk DNA but he just ran out of time.

I don't know if there were questions but, if there were, I bet that none of them challenged Mattick's main thesis.

Seeking Postdoc in Statistical Genetics and Infectious Disease

I am seeking a senior postdoc in Statistical Genetics and Infectious Disease to join my research group at the Big Data Institute, University of Oxford. Our research into Infectious Disease Genomics is focused on developing and applying big data methods to identify genetic risk factors for disease, both microbial virulence factors and human susceptibility genes. We are focused on a range of bacterial and viral diseases including staphylococcal sepsis and COVID-19.

The Big Data Institute, part of Oxford Population Health, provides an excellent environment for multi-disciplinary research and teaching. Situated on the modern Old Road Campus in the heart of the medical sciences neighbourhood of Headington, we benefit from outstanding facilities and opportunities to collaborate with world-leading scientists and clinicians to help expand knowledge and improve global health.

As a Senior Postdoc the post-holder will work closely with me to jointly lead the implementation, design and application of new statistical tools for genome-wide association studies, and to lead the biological interpretation of key findings. They will develop novel methodologies for analysis and data collection, take the lead in the production of scientific reports and publications and supervise junior group members.

To be considered applicants will have a PhD and post-doctoral experience in a relevant subject, with direct experience in statistical genetics, demonstrable expertise and knowledge of the statistical genetics literature or a closely related, relevant discipline and a publication record as first author, in statistical genetics.

The position is full time (part time considered) and fixed-term for 3 years.

The closing date for application is 12.00 noon GMT on 18th March.

Click here for more information including how to apply.

Should we teach genomics and evolution to medical students?

Rama Singh,1 a biology professor at McMaster Universtiy in Hamilton (Ontario, Canada) has just published an interesting article on The Conversation website. It's about Medical schools need to prepare doctors for revolutionary advances in genetics. You can read the full article yourself but let me highlight the last few paragraphs to start the discussion.

Future physicians will be part of health networks involving medical lab technicians, data analysts, disease specialists and the patients and their family members. The physician would need to be knowledgeable about the basic principles of genetics, genomics and evolution to be able to take part in the chain of communication, information sharing and decision-making process.

This would require a more in-depth knowledge of genomics than generally provided in basic genetics courses.

Much has changed in genetics since the discovery of DNA, but much less has changed how genetics and evolution are taught in medical schools.

In 2013-14 a survey of course curriculums in American and Canadian medical schools showed that while most medical schools taught genetics, most respondents felt the amount of time spent was insufficient preparation for clinical practice as it did not provide them with sufficient knowledge base. The survey showed that only 15 per cent of schools covered evolutionary genetics in their programs.

A simple viable solution may require that all medical applicants entering medical schools have completed rigorous courses in genetics and genomics.

Here's the problem. I've just finished research on a book about modern evolution and genomics so I think I know a little bit about the subject. I'm also on the editorial board of a journal that publishes research on biochemistry and molecular biology education. I've written a biochemistry textbook and I have far too many years of experience trying to teach this material to graduate students and undergraduates at the University of Toronto. I can safely say that we (university teachers) have done a horrible job of teaching evolution and genomics to our students. We have turned out an entire generation of students who don't understand modern molecular evolution and don't understand what's in your genome.

What this means is that there's an extremely small pool of students who have completed "rigorous courses in genetics and genomics." Nobody will be able to apply to medical school. I doubt that we could teach this material to medical students with or without the appropriate background.

But you don't have to take my word for it. Some people have tried to teach this material to health science workers so we can see how it's working at that level. Take a look at the The Genomics Education Programme supported by the NHS in the United Kingdom. They have a series of short videos and longer lessons that are designed to educate health care specialists. Here's the blurb that defines their objective.

Rapid advances in technology and understanding mean that genomics is now more relevant than ever before. As genomics increasingly becomes a part of mainstream NHS care, all healthcare professionals, and not just genomics specialists, need to have a good understanding of its relevance and potential to impact the diagnosis, treatment and management of people in our care.

In 2014, Health Education England (HEE) launched a four-year £20 million Genomics Education Programme (GEP) to ensure that our 1.2 million-strong NHS workforce has the knowledge, skills and experience to keep the UK at the heart of the genomics revolution in healthcare.

Funding for the programme has since been extended to enable us to continue our work in providing co-ordinated national direction of education and training in genomics and developing resources for a wide range of professionals.

They describe genes as 'coding' genes that build proteins. There's no mention of noncoding genes. The define a genome as "both genes (coding) and non-coding DNA." They also say that your genome is all of the DNA in our cells (46 chromosomes, 23 pairs). I don't see anything in their education packages that covers modern molecular evolution. In one of the packages they say,

The term ‘junk DNA’ has been used since the 1970s to describe non-coding regions of the genome, but today it is considered inaccurate and misleading. The term ‘junk’ suggests that 98% of the genome has no use, but in recent years, studies and projects have used advances in technology to shed light on these regions and have come to different conclusions about how much of the genome has a biological function.

Here's a link to a short video called What is a genome?. I recommend that you watch it to see the level that these experts think is suitable for health care professionals in the UK and to see the level of expertise of those who made the video. This is what seven years of work by experts and £20 million will get you.

All of this tells me that teaching genomics and evolution to medical students is going to be a lot more difficult than Rama Singh imagines. Not only would we have to counter several years of misinformation but we would have to rely on teachers who probably don't understand either topic.

Let's start by teaching these things correctly to biology and biochemistry majors. That's going to be hard enough for now.

1. Full displosure: Rama and I shared an NSERC grant in 1981 on genetic variation in Drosophila.

On the accuracy of genomics in detecting disease variants

Several diseases, such as cancers, are caused by the presence of deleterious alleles that affect the function of a gene. In the case of cancer, most of the mutations are somatic cell mutations—mutations that have occurred after fertilization. These mutations will not be passed on to future generations. However, there are some variants that are present in the germline and these will be inherited. A small percentage of these variants will cause cancer directly but most will just indicate a predisposition to develop cancer.

There are a host of other diseases that have a genetic component and the responsible alleles can also be present in the germline or due to somatic cell mutations.

Over the past fifty years or so there has been a lot of hype associated with the latest technological advances and the ability to detect deleterious germline mutations. The general public has been repeatedly told that we will soon be able to identify all disease-causing alleles and this will definitely lead to incredible medical advances in treating these diseases. Just yesterday, for example, I posted an article on predictions made by The National Genome Research Institute (USA) who predicts that by 2030,

The clinical relevance of all encountered genomic variants will be readily predictable, rendering the diagnostic designation ‘variant of uncertain significance (VUS)’ obsolete.

Similar predictions, in various forms, were made when the human genome project got under way and at various time afterword. First there was the 1000 genomes project then there was the 100,000 genome project and, of course, ENCODE. The problem is that genomics hasn't lived up to these expectations and there's a very good reason for that: it's because the problem is a lot more difficult than it seems.

One of the Facebook groups that I follow (Modern Genetics & Technology)1 alerted me to a recent paper in JAMA that addressed the problem of genomics accuracy and the prediction of pathogenic variants. I'm posting the complete abstract so you can see the extent of the problem.

AlDubayan, S.H., Conway, J.R., Camp, S.Y., Witkowski, L., Kofman, E., Reardon, B., Han, S., Moore, N., Elmarakeby, H. and Salari, K. (2020) Detection of Pathogenic Variants With Germline Genetic Testing Using Deep Learning vs Standard Methods in Patients With Prostate Cancer and Melanoma. JAMA 324:1957-1969. [doi: 10.1001/jama.2020.20457]

Importance Less than 10% of patients with cancer have detectable pathogenic germline alterations, which may be partially due to incomplete pathogenic variant detection.

Objective To evaluate if deep learning approaches identify more germline pathogenic variants in patients with cancer.

Design Setting, and Participants A cross-sectional study of a standard germline detection method and a deep learning method in 2 convenience cohorts with prostate cancer and melanoma enrolled in the US and Europe between 2010 and 2017. The final date of clinical data collection was December 2017.

Exposures Germline variant detection using standard or deep learning methods.

Main Outcomes and Measures The primary outcomes included pathogenic variant detection performance in 118 cancer-predisposition genes estimated as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The secondary outcomes were pathogenic variant detection performance in 59 genes deemed actionable by the American College of Medical Genetics and Genomics (ACMG) and 5197 clinically relevant mendelian genes. True sensitivity and true specificity could not be calculated due to lack of a criterion reference standard, but were estimated as the proportion of true-positive variants and true-negative variants, respectively, identified by each method in a reference variant set that consisted of all variants judged to be valid from either approach.

Results The prostate cancer cohort included 1072 men (mean [SD] age at diagnosis, 63.7 [7.9] years; 857 [79.9%] with European ancestry) and the melanoma cohort included 1295 patients (mean [SD] age at diagnosis, 59.8 [15.6] years; 488 [37.7%] women; 1060 [81.9%] with European ancestry). The deep learning method identified more patients with pathogenic variants in cancer-predisposition genes than the standard method (prostate cancer: 198 vs 182; melanoma: 93 vs 74); sensitivity (prostate cancer: 94.7% vs 87.1% [difference, 7.6%; 95% CI, 2.2% to 13.1%]; melanoma: 74.4% vs 59.2% [difference, 15.2%; 95% CI, 3.7% to 26.7%]), specificity (prostate cancer: 64.0% vs 36.0% [difference, 28.0%; 95% CI, 1.4% to 54.6%]; melanoma: 63.4% vs 36.6% [difference, 26.8%; 95% CI, 17.6% to 35.9%]), PPV (prostate cancer: 95.7% vs 91.9% [difference, 3.8%; 95% CI, –1.0% to 8.4%]; melanoma: 54.4% vs 35.4% [difference, 19.0%; 95% CI, 9.1% to 28.9%]), and NPV (prostate cancer: 59.3% vs 25.0% [difference, 34.3%; 95% CI, 10.9% to 57.6%]; melanoma: 80.8% vs 60.5% [difference, 20.3%; 95% CI, 10.0% to 30.7%]). For the ACMG genes, the sensitivity of the 2 methods was not significantly different in the prostate cancer cohort (94.9% vs 90.6% [difference, 4.3%; 95% CI, –2.3% to 10.9%]), but the deep learning method had a higher sensitivity in the melanoma cohort (71.6% vs 53.7% [difference, 17.9%; 95% CI, 1.82% to 34.0%]). The deep learning method had higher sensitivity in the mendelian genes (prostate cancer: 99.7% vs 95.1% [difference, 4.6%; 95% CI, 3.0% to 6.3%]; melanoma: 91.7% vs 86.2% [difference, 5.5%; 95% CI, 2.2% to 8.8%]).

Conclusions and Relevance Among a convenience sample of 2 independent cohorts of patients with prostate cancer and melanoma, germline genetic testing using deep learning, compared with the current standard genetic testing method, was associated with higher sensitivity and specificity for detection of pathogenic variants. Further research is needed to understand the relevance of these findings with regard to clinical outcomes.

It's really difficult to understand this paper since there are many terms that I'd have to research more thoroughly; for example, does "germline whole-exon sequencing" mean that only sperm or egg DNA was sequenced and that every single exon in the entire genome was sequenced? Were exons in noncoding genes also sequenced?

I found it much more useful to look at the accompanying editorial by Gregory Feero.

Feero, W.G. (2020) Bioinformatics, Sequencing Accuracy, and the Credibility of Clinical Genomics. JAMA 324:1945-1947. [doi: 10.1001/jama.2020.19939]

Ferro explains that the main problem is distinguishing real pathogenic variants from false positives and this can only be accomplished by first sequencing and assembling the DNA and then using various algorithms to focus on important variants. Then there's the third step.

The third step, which often requires a high level of clinical expertise, sifts through detected potentially deleterious variations to determine if any are relevant to the indication for testing. For example, exome sequencing ordered for a patient with unexplained cardiomyopathy might harbor deleterious variants in the BRCA1 gene which, while a potentially important incidental finding, does not provide a plausible molecular diagnosis for the cardiomyopathy. The complexity of the bioinformatics tools used in these 3 steps is considerable.

It's that third step that's analyzed in the AlDubayan et al. paper and one of the tools used is a deep-learning (AI) algorithm. However, the training of this algorithm requiries considerable clinical expertise and testing it requires a gold standard set of variants to serve as an internal control. As you might have guessed, that gold standard doesn't exist because the whole point of the genomics is to identify perviously unknown deleterious alleles.

Ferro warns us that "clinical genome sequencing remains largely unregulated and accuracy is highly dependant on the expertise of individual testing laboratories." He concludes that genomics still has a long way to go.

The genomics community needs to act as a coherent body to ensure reproducibility of outcomes from clinical genome or exome sequencing, or provide transparent quality metrics for individual clinical laboratories. Issues related to achieving accuracy are not new, are not limited to bioinformatics tools, and will not be surmounted easily. However, until analytic and clinical validity are ensured, conversations about the potential value that genome sequencing brings to clinical situations will be challenging for clinical centers, laboratories that provide sequencing services, and consumers. For the foreseeable future, nongeneticist clinicians should be familiar with the quality of their chosen genome-sequencing laboratory and engage expert advice before changing patient management based on a test result.

I'm guessing that Gregory Feero doesn't think that in nine years (2030) "The clinical relevance of all encountered genomic variants will be readily predictable."

1. I do NOT recommend this group. It's full of amateurs who resist leaning and one of it's main purposes is to post copies of pirated textbooks in its files. The group members get very angry when you tell them that what they are doing is illegal!

RefSeq Release 205 is available!

RefSeq release 205 is now available online, from the FTP site and through NCBI’s Entrez programming utilities, E-utilities. This full release incorporates genomic, transcript, and protein data available as of March 1, 2021, and contains 269,975,565 records, including 197,232,209 proteins, 36,514,168 RNAs, and sequences from 108,257  organisms. The release is provided in several directories as a complete dataset … Continue reading RefSeq Release 205 is available!

Postdoc position available in Statistical Genomics

I am seeking someone with a track record in methods development for Statistical Genomics and an interest in Infectious Disease to join the group. The aim of the post is to conduct innovative research within the group's range of interests and to make use of the opportunities afforded by our outstanding collaborators. I would welcome candidates who wish to use the opportunity as a stepping stone to independent funding.

The postdoc will join a team with expertise in microbiology, genomics, evolution, population genetics and statistical inference. Responsibilities will include planning a research project and milestones with help and guidance from the group, preparing manuscripts for publication, keeping records of results and methods and tracking milestones, and disseminating results, including through academic conferences.

We will consider applicants who hold, or are close to completion of, a PhD/DPhil involving statistical methods development, and who have experience of large-scale statistical data analysis, evidence of originating and executing independent academic research ideas, excellent interpersonal skills and the ability to work closely with others in a team.

The position is advertised to 31 December 2021. The application deadline is noon on Thursday 1st October 2020. Visit the University recruitment page to apply.

Improved access to SARS-CoV-2 data

NCBI Datasets has a simple, new way to get Coronoviridae data, including from SARS-CoV-2 (Figure 1). The data package includes genomic, protein and CDS sequences, annotation and a comprehensive data report for all complete genomes. You can also target your search … Continue reading

May 20 webinar: Exploring SRA metadata in the cloud with BigQuery

Join us on May 20th to learn how to use Google’s BigQuery to quickly search the data from the Sequence Read Archive (SRA) in the cloud to speed up your bioinformatic research and discovery projects. BigQuery is a tool for … Continue reading