BGI strikes back at Illumina in tit-for-tat patent infringement battle

A subsidiary of the Chinese sequencing giant BGI is filing a patent infringement suit agains the US sequencing giant. The BGI subsidiary, Complete Genomics, filed its complaint in the the District Court of Deleware, claiming infringement on a patent for “methods and compositions for efficient base calling in sequencing reactions,” which is all pretty central to high throughput sequencing. This appears to be a response to Illumina filing a complaint earlier this month against BGI Europe and Latvia MGI Tech, another BGI subsidiaries, earlier this year. These are probably just the early stages of what is likely to be a long series of antagonistic maneuvers between giants. It is unlikely that this Coke-versus-Pepsi style competition will do much to reduce the dominance of these groups, but one can hope that as this battle plays out some of the smaller sequencing players will grow and insert a little more competition into the market.

All the links in this post are from Genome-Web, another excellent genome news source.

Categorizing Cells with Machine Learning and Latent Space

Two exciting and complementary machine learning methods for assigning cell identity based on single-cell sequencing data were published in a paper from Johns Hopkins. The first program, scCoGAPS, defines latent spaces from a single-cell RNA-sequencing dataset to categorize cells and the second program, projectR, evaluates latent spaces in independent target datasets using transfer learning. These two methods are interesting advances towards a goal that is likely still far off—understanding exactly what makes each cell what it is. For an excellent summary read the press release, Finding A Cell’s True Identity.

The original article is a more complicated reading but interesting through out.

Stein-O’Brien, et al. (2019) Decomposing Cell Identity for Transfer Learning across Cellular Measurements, Platforms, Tissues, and Species. Cell Systems

Summary

Analysis of gene expression in single cells allows for decomposition of cellular states as low-dimensional latent spaces. However, the interpretation and validation of these spaces remains a challenge. Here, we present scCoGAPS, which defines latent spaces from a source single-cell RNA-sequencing (scRNA-seq) dataset, and projectR, which evaluates these latent spaces in independent target datasets via transfer learning. Application of developing mouse retina to scRNA-Seq reveals intrinsic relationships across biological contexts and assays while avoiding batch effects and other technical features. We compare the dimensions learned in this source dataset to adult mouse retina, a time-course of human retinal development, select scRNA-seq datasets from developing brain, chromatin accessibility data, and a murine-cell type atlas to identify shared biological features. These tools lay the groundwork for exploratory analysis of scRNA-seq data via latent space representations, enabling a shift in how we compare and identify cells beyond reliance on marker genes or ensemble molecular identity.

Human Genome Reference Sequence: Summary or Example?

There is no one human genome. Each person starts life with two non-identical copies of a genome, and variations both small and large begin to accumulate each time those copies are copied. And then there are the differences between individuals. If we think of the genome as a single list of bases at specific positions then point mutations—substitutions, small inserts and deletions—are easy enough to map to those position, however major structural variants—inversions, translocations and repetitive sequences—complicate how we map these mutations. Reference genomes, a consensus representation of deeply sequenced human genomes have traditionally been the basis of how we map nucleotides and variants to positions on chromosomes but long read technologies are making it increasingly apparent that structural variants are quite common and new methods for representing the human genome.

The first of the following articles lays out why a more advanced model for capturing the variation in the human genome is needed. The article after that describes how multiple genomes and their structural variation can be summarized using graphs, a computational improvement on the current linear reference genomes. The last article discusses the some of the single molecule sequencing technology bringing this issue to the fore. There are many other articles that deal with this topic, but these are a good start.

Yang, et al. (2019) One reference genome is not enough. Genome Biology

Abstract

A recent study on human structural variation indicates insufficiencies and errors in the human reference genome, GRCh38, and argues for the construction of a human pan-genome.

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Here’s an article describing how structural variants can be captured in a graph.

Rakocevic, et al. (2019) Fast and accurate genomic analyses using genome graphs. Nature Genetics

Abstract

The human reference genome serves as the foundation for genomics by providing a scaffold for alignment of sequencing reads, but currently only reflects a single consensus haplotype, thus impairing analysis accuracy. Here we present a graph reference genome implementation that enables read alignment across 2,800 diploid genomes encompassing 12.6 million SNPs and 4.0 million insertions and deletions (indels). The pipeline processes one whole-genome sequencing sample in 6.5 h using a system with 36 CPU cores. We show that using a graph genome reference improves read mapping sensitivity and produces a 0.5% increase in variant calling recall, with unaffected specificity. Structural variations incorporated into a graph genome can be genotyped accurately under a unified framework. Finally, we show that iterative augmentation of graph genomes yields incremental gains in variant calling accuracy. Our implementation is an important advance toward fulfilling the promise of graph genomes to radically enhance the scalability and accuracy of genomic analyses.

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Here’s an article describing how next-next generation sequencing is illuminating the diversity of structural variants across human populations.

Chaisson, et al. (2015) Resolving the complexity of the human genome using single-molecule sequencing. Nature

Abstract

Advances in genome assembly and phasing provide an opportunity to investigate the diploid architecture of the human genome and reveal the full range of structural variation across population groups. Here we report the de novo assembly and haplotype phasing of the Korean individual AK1 (ref. 1) using single-molecule real-time sequencing2, next-generation mapping3, microfluidics-based linked reads4, and bacterial artificial chromosome (BAC) sequencing approaches. Single-molecule sequencing coupled with next-generation mapping generated a highly contiguous assembly, with a contig N50 size of 17.9 Mb and a scaffold N50 size of 44.8 Mb, resolving 8 chromosomal arms into single scaffolds. The de novoassembly, along with local assemblies and spanning long reads, closes 105 and extends into 72 out of 190 euchromatic gaps in the reference genome, adding 1.03 Mb of previously intractable sequence. High concordance between the assembly and paired-end sequences from 62,758 BAC clones provides strong support for the robustness of the assembly. We identify 18,210 structural variants by direct comparison of the assembly with the human reference, identifying thousands of breakpoints that, to our knowledge, have not been reported before. Many of the insertions are reflected in the transcriptome and are shared across the Asian population. We performed haplotype phasing of the assembly with short reads, long reads and linked reads from whole-genome sequencing and with short reads from 31,719 BAC clones, thereby achieving phased blocks with an N50 size of 11.6 Mb. Haplotigs assembled from single-molecule real-time reads assigned to haplotypes on phased blocks covered 89% of genes. The haplotigs accurately characterized the hypervariable major histocompatability complex region as well as demonstrating allele configuration in clinically relevant genes such as CYP2D6. This work presents the most contiguous diploid human genome assembly so far, with extensive investigation of unreported and Asian-specific structural variants, and high-quality haplotyping of clinically relevant alleles for precision medicine.

Thank you for reading!

Personalized medicine approaches designed with Fruit Flies

The promise of personalized medicine has been limited by at least two factors: 1) the power of our models—including our ability to get and process data, and 2) our ability to test potential therapeutic solutions in meaningful biological systems, rapidly and systematically, before testing in humans. While we’re seeing rapid and predictable improvements with the power of our models corresponding improvements in how we test therapies have been less consistent. Even if you have a good, genome-level understanding of an individual’s cancer, there is no guidebook for what treatment will be effective against that particular type of cancer. One promising approach has been to use fruit flies (Drosophila) genetically-modified with similar mutational loads as cancer patients to test the efficacy of drug combinations for suppressing tumor growth.

Ross Cagan is an exceptionally creative researcher who leads a lab at the Icahn School of Medicine at Mount Sinai and has been using fruit flies to develop personalized medicine approaches for over 10 years. This week, the Cagan lab published one of the first examples of clinically effective therapies based on such an approach, with lead author Dr. Erdem Bangi (see below for abstract and link). This article describes how Drosophila were screened for effective drug combinations for inhibiting tumors with a similar composition and complexity as a terminally ill colorectal cancer patient’s tumors, and how a specific drug combination was identified and used to effectively shrink the patient’s tumors. Though this treatment did not provide a permanent cure, it did appear to extend the patient’s life and is important proof that this type of approach can be effective.

Excellent, more in-depth summaries can be found HERE and HERE and HERE, so check them out.

The original article, with its somewhat difficult to penetrate title…

Bangi, E., et al. (2019) A personalized platform identifies trametinib plus zoledronate for a patient with KRAS-mutant metastatic colorectal cancer. Science Advances

Abstract

Colorectal cancer remains a leading source of cancer mortality worldwide. Initial response is often followed by emergent resistance that is poorly responsive to targeted therapies, reflecting currently undruggable cancer drivers such as KRAS and overall genomic complexity. Here, we report a novel approach to developing a personalized therapy for a patient with treatment-resistant metastatic KRAS-mutant colorectal cancer. An extensive genomic analysis of the tumor’s genomic landscape identified nine key drivers. A transgenic model that altered orthologs of these nine genes in the Drosophila hindgut was developed; a robotics-based screen using this platform identified trametinib plus zoledronate as a candidate treatment combination. Treating the patient led to a significant response: Target and nontarget lesions displayed a strong partial response and remained stable for 11 months. By addressing a disease’s genomic complexity, this personalized approach may provide an alternative treatment option for recalcitrant disease such as KRAS-mutant colorectal cancer.

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Thank you for reading!

Polygenic traits should not be used for selecting embryos

The article below is an important perspective on the troubling potential use of polygenic trait scores to select embryos, written by one of the directors of the EMBL-EBI on his blog. Polygenic traits are directly affected by several loci and typically exhibit phenotypes that have continuous distributions, such as intelligence and height. While some pretty obvious arguments can be made for why using polygenic traits for selecting embryos would be immoral, this article helps to make clear that it would also likely be an ineffective way to guarantee your child has a certain height and IQ.

Polygenic trait scores, their value to medicine and for making predictions about humans, is being discussed very actively right now. Some of the most exciting, real-time conversations about polygenic traits and polygenic risk scores are happening on Twitter in real time. I strongly encourage you to follow Ewan Birney (@ewanbirney) and Cecile Janssens (@cecilejanssens) professor of translational epidemiology at Emory University, for her consistently clear and insightful comments on how we interpret whole genome data.

Why embryo selection for polygenic traits is wrong.

MAY 26, 2019 BY EWANBIRNEY

This week (May 20th 2019) has seen yet another splash by an American company offering a polygenic trait score on embryos including intelligence. This is wrong on a number of levels; ethically it is wrong to make this decision as an independent laboratory without broad societal buy in; scientifically it is wrong to imagine the ways we assess polygenic traits will translate into safe and effective embryo selection; for the specifics of IQ/Educational attainment trait this trait is so complex this is additionally unwise over and above any concerns.

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When is the right time to have your child's genome sequenced?

Every new technology will raise questions about its potential and effects, especially in regards to children. So it should be no surprise that now, as genome sequencing technology becomes cheaper, better, and more accessible to consumers, some important and sometimes impassioned conversations are going to have to happen. For some perspective, people had grave concerns about kids playing Dungeons & Dragons when it first became popular. The article below does a nice job covering some important points to consider.

Now You Can Genetically Test Your Child For Disease Risks. Should You?

Genomics is cheaper and more available then ever, but its usefulness for parents has yet to be proven…

“Most direct-to-consumer genetic testing services still require that patients be at least 18 years old. But there are workarounds. The popular at-home DNA test 23andMe requires that users be 18, but parents can order $199 kits for their offspring and send back their saliva through the mail, according to spokesman Andy Kill. (Kill says the company doesn't have statistics on how many children’s samples it has received.) And in April, the FDA ruled that 23andMe could release reports about patients’ risks for diseases, including Parkinson’s and late-onset Alzheimer’s diseases.

As testing children for genetic diseases becomes available to more parents, it is raising difficult ethical questions. For instance: Would the knowledge that your kid might get sick someday make you treat them differently? “There’s a concern that parents might connect to kids in a different way if they knew something negative about their future,” says Laventhal. Perhaps you'd be proactive by pushing your daughters to freeze their eggs at a young age, if you knew they were at risk for cancers and might undergo cancer treatments that could hurt their fertility.

“You’re going to create a lot of unnecessary stress and anxiety and make parents crazy,” adds Dr. Lainie Friedman Ross, who researches genetic testing policy at the MacLean Center for Clinical Medical Ethics at the University of Chicago. “

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Bold Chinese Experiment Genetically Engineers Monkeys, maybe makes them Smarter, definitely raises some ethical questions

Chinese researchers are going hard lately! Following projects that include genetically modifying embryos and letting two develop into twin human babies, and cloning primates, another envelope-pushing report comes from the Chinese Bio-Science community—this time by inserting a human version of a gene into a Rhesus Monkey. The gene MCPH1 is thought to play an important role in human brain development and contribute to the distinctively human cognitive ability. The genetically modified monkeys exhibited slower (more human-like) brain development and possibly even improved cognitive ability. This work was published by Oxford University Press on behalf of China Science Publishing & Media Ltd., which is ostensibly a peer-review journal, but not PLOS or PNAS, and it unclear if this work would be given the green light at an American University. There will certainly be debate in the press about this topic, which should be thrilling, but hopefully it will hasten a some thoughtful conclusions.

Read the original article HERE and other summaries here and here and here.

The Human Family Tree is a Bush

More evidence of branching off and reconnecting in the early history of humans

It has been an exciting week week for human ancestry. First, a new species of hominid was identified in the Philippines, Homo luzonensis, and now there’s evidence of the formerly elusive Denisovans in the ancient ancestry of Papuans. Adding to the excitement, this group found evidence of at least three distinct Denisovan lineages, and that humans likely interbred with Denisovan cousins somewhere around New Guinea. This is all pretty amazing, considering we first became aware of Denisovans from a single DNA sample from a finger, found in a cave, in Siberia.

Multiple Deeply Divergent Denisovan Ancestries in Papuans

Jacobs et.al, Cell (Research Article)

Highlights

•A new dataset of 161 genomes covering the understudied Indonesia-New Guinea region

•Introgressing Denisovans comprise at least three genetically divergent groups

•Papuans carry haplotypes from two Denisovan groups, with one unique to Oceania

•Some Denisovan introgression was recent and likely occurred in New Guinea or Wallacea

Summary—Genome sequences are known for two archaic hominins—Neanderthals and Denisovans—which interbred with anatomically modern humans as they dispersed out of Africa. We identified high-confidence archaic haplotypes in 161 new genomes spanning 14 island groups in Island Southeast Asia and New Guinea and found large stretches of DNA that are inconsistent with a single introgressing Denisovan origin. Instead, modern Papuans carry hundreds of gene variants from two deeply divergent Denisovan lineages that separated over 350 thousand years ago. Spatial and temporal structure among these lineages suggest that introgression from one of these Denisovan groups predominantly took place east of the Wallace line and continued until near the end of the Pleistocene. A third Denisovan lineage occurs in modern East Asians. This regional mosaic suggests considerable complexity in archaic contact, with modern humans interbreeding with multiple Denisovan groups that were geographically isolated from each other over deep evolutionary time.

Read the original article HERE … and other summaries here and here.

Promising result in Cancer Vaccine Clinical Trial

Mount Sinai Researchers Develop Treatment That Turns Tumors Into Cancer Vaccine Factories

Researchers at Mount Sinai have developed a novel approach to cancer immunotherapy, injecting immune stimulants directly into a tumor to teach the immune system to destroy it and other tumor cells throughout the body. 

The “in situ vaccination” worked so well in patients with advanced-stage lymphoma that it is also undergoing trials in breast and head and neck cancer patients, according to a study published in Nature Medicine in April.

The treatment consists of administering a series of immune stimulants directly into one tumor site.  The first stimulant recruits important immune cells called dendritic cells that act like generals of the immune army. The second stimulant activates the dendritic cells, which then instruct T cells, the immune system’s soldiers, to kill cancer cells and spare non-cancer cells. This immune army learns to recognize features of the tumor cells so it can seek them out and destroy them throughout the body, essentially turning the tumor into a cancer vaccine factory.

“The in situ vaccine approach has broad implications for multiple types of cancer,” said lead author Joshua Brody, MD, Director of the Lymphoma Immunotherapy Program at The Tisch Cancer Institute at the Icahn School of Medicine at Mount Sinai. “This method could also increase the success of other immunotherapies such as checkpoint blockade.”

After testing the lymphoma vaccine in the lab, it was tested in 11 patients in a clinical trial. Some patients had full remission from months to years. In lab tests in mice, the vaccine drastically increased the success of checkpoint blockade immunotherapy, the type of immunotherapy responsible for the complete remission of former President Jimmy Carter’s cancer and the focus of the 2018 Nobel Prize in Medicine.

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Thank you for reading!

Jumping genes are the exciting part of the Poison Frog Genome

The genome of the strawberry poison frog, Oophaga pumilio, has just been sequenced and the results show that it hosts a wide array of transposable elements, “jumping genes,” virus-like, repetitive sequences that copy themselves inside of genomes. The poison frog has a genome that is twice the size of the human genome, and two-thirds of it is composed of transposable elements. In addition, there is evidence that many of these transposable elements have recently horizontally transferred into the genome.

Genomic Takeover by Transposable Elements in the Strawberry Poison Frog

Rogers RL, Zhou L, Chu C, Márquez R, Corl A, Linderoth T, Freeborn L, MacManes MD, Xiong Z, Zheng J, Guo C, Xun X, Kronforst MR, Summers K, Wu Y, Yang H, Richards-Zawacki CL, Zhang G2, & Nielsen R

Abstract—We sequenced the genome of the strawberry poison frog, Oophaga pumilio, at a depth of 127.5× using variable insert size libraries. The total genome size is estimated to be 6.76 Gb, of which 4.76 Gb are from high copy number repetitive elements with low differentiation across copies. These repeats encompass DNA transposons, RNA transposons, and LTR retrotransposons, including at least 0.4 and 1.0 Gb of Mariner/Tc1 and Gypsy elements, respectively. Expression data indicate high levels of gypsy and Mariner/Tc1 expression in ova of O. pumilio compared with Xenopus laevis. We further observe phylogenetic evidence for horizontal transfer (HT) of Mariner elements, possibly between fish and frogs. The elements affected by HT are present in high copy number and are highly expressed, suggesting ongoing proliferation after HT. Our results suggest that the large amphibian genome sizes, at least partially, can be explained by a process of repeated invasion of new transposable elements that are not yet suppressed in the germline. We also find changes in the spliceosome that we hypothesize are related to permissiveness of O. pumilio to increases in intron length due to transposon proliferation. Finally, we identify the complement of ion channels in the first genomic sequenced poison frog and discuss its relation to the evolution of autoresistance to toxins sequestered in the skin.

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Thank you for reading!

The first step towards a "Cancer Dependency Map"

That most cancers use the same set of molecular tools is a very powerful idea, but it has been hard to figure out what these tools are and how to target them. Follow the link below for a quick, and enthusiastic, summary of genome-scale CRISPR–Cas9 screens of 324 human cancer cell lines from 30 cancer types with the goal of developing a new, diverse and more effective portfolio of cancer drug targets.

'Dismantling cancer' reveals weak spots

James Gallagher, BBC News

Scientists have taken cancer apart piece-by-piece to reveal its weaknesses, and come up with new ideas for treatment. A team at the Wellcome Sanger Institute disabled every genetic instruction, one at a time, inside 30 types of cancer. It has thrown up 600 new cancer vulnerabilities and each could be the target of a drug.Cancer Research UK praised the sheer scale of the study.

The study heralds the future of personalised cancer medicine. At the moment drugs like chemotherapy cause damage throughout the body. One of the researchers is Dr Fiona Behan, whose mother died after getting cancer for the second time.

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Another helpful summary of the recent paper linking jumping genes to cancer

A recent post discussed an important paper that demonstrated an association between transposable elements, aka ‘jumping genes’, and cancer. Transposable elements are an important but often forgotten class of mutagen that can contribute to genome instability and may disrupt genes and their expression. The original article has an outstanding abundance of data and is not easy reading. The article below provides an accessible summary that’s worth reading if you aren’t an expert in genomics.

Cancer: Scientists find 129 'jumping genes' that drive tumor growth

Catharine Paddock, Medical News Today

In cancer research, scientists usually look for cancer genes by scouring the genome for altered sequences — or mutations — in DNA. But a new study has now revealed that jumping genes, which customary sequencing overlooks, are also important drivers of tumor growth.

Scientists at the Washington University School of Medicine in St. Louis, MO, found that jumping genes are widespread in cancer and promote tumor growth by forcing cancer genes to remain switched on.

They analyzed 7,769 tumor samples from 15 different types of cancer and found 129 jumping genes that can drive tumor growth through their influence on 106 different cancer genes.

The jumping genes were functioning as "stealthy on-switches" in 3,864 of the tumors that the team analyzed. These tumors came from breast, colon, lung, skin, prostate, brain, and other types of cancer.

A recent Nature Genetics paper gives a full account of the study.

CRISPR gene drive vs CRISPR allelic drive

CRISP-based genome modifying technologies are offer a power and precision people only dreamed of not that long ago. CRISPR gene drives use guide RNAs (gDNAs) to insert gene-drive sequences, and the CRISPR allele drives do the same while also modifying undesired variants at a second position. Gene and allele drives are likely to be central to how humans modify the living environment in the future, in addition to being the starting point for endless unchecked-tech sci-fi nightmare scenarios . The following article provides a clear and helpful explanation of these technologies and some of their applications.

CRISPR-based 'allelic drive' allows genetic editing with selective precision and broad implications

Scientists developed a new version of a gene drive that allows the spread of specific, favorable genetic variants, also known as 'alleles,' throughout a population. The new 'allelic drive' is equipped with a guide RNA that directs CRISPR to cut undesired variants of a gene and replace it with a preferred version. Using a word processing analogy, CRISPR-based gene drives allow scientists to edit sentences of genetic information, while the new allelic drive offers letter-by-letter editing.

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Cancer signaling studies take a page from Genetics methods

The following article provides insights into the promise of applying quantitative approaches in the context of tumor tissues and clinical environments. Genetics approaches have dominated cancer research because they generate such an abundance of data ( and because the methodology is so widely and readily generalizable). While genetic variations clearly play an important role in cancer, deviant signaling drives cancer progression and signaling molecules are the targets of most chemotherapeutics. This highlights the importance of understanding cancer signaling pathways with data-rich and quantitatively rigorous methods, similar to those used in genetics. The following article in Science Signaling discusses this topic and is both thorough and accessible. —RPR

Why geneticists stole cancer research even though cancer is primarily a signaling disease

Michael B. Yaffe, Science Signaling

Abstract—Genetic approaches to cancer research have dramatically advanced our understanding of the pathophysiology of this disease, leading to similar genetics-based approaches for precision therapy, which have been less successful. Reconfiguring and adapting the types of technologies that underlie genetic research to dissect tumor cell signaling in clinical samples may offer an alternative road forward.

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Thank you for reading!

Population Structure: A Key Concept for Understanding Genetic Variation

It is common for articles to claim that “the gene for” some trait or disease has been identified. Usually they actually mean that an association has been found between an uncommon genetic variant found in, or near, a gene and some trait or disease. These kinds of articles are becoming increasingly common because Genome-Wide Association Studies (GWAS) are becoming cheaper and more common. Though GWAS yield important insights their results can be misleading because ancestral relationships between individuals in the study can create signals that can be misinterpreted as association with the trait being studied. This phenomenon is very powerful and one reason why it is important to have a diverse group of individuals in any genetic study. Underlying ancestral relationships are known as “population structure” and serious thought is required to ensure that it doesn’t skew GWAS results. The paper below is a scientific review article (in an excellent journal with exceptional authors) and not exactly easy reading, but it was written for a broad audience and worth considering the next time you see an article discussing the identification of “the genes for” something or other, even if it appears in Genome-Media.

-RPR

Population Genetics: Why structure matters

Abstract

Great care is needed when interpreting claims about the genetic basis of human variation based on data from genome-wide association studies.

Main text

Human height is the classic example of a quantitative trait: its distribution is continuous, presumably because it is influenced by variation at a very large number of genes, most with a small effect (Fisher, 1918). Yet height is also strongly affected by the environment: average height in many countries increased during the last century and the children of immigrants are often taller than relatives in their country of origin – in both cases presumably due to changing diet and other environmental factors (Cavalli-Sforza and Bodmer, 1971; Grasgruber et al., 2016; NCD Risk Factor Collaboration, 2016). This makes it very difficult to determine the cause of geographic patterns for height, such as the ‘latitudinal cline’ seen in Europe (Figure 1).

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Advances in Single Cell RNA-Sequencing

Single cell transcriptomics: A new sequencing approach

“Researchers from University of Southern Denmark, Wellcome Sanger Institute and BGI, today published a study in the journal Genome Biology comparing the library preparation and sequencing platforms for single-cell RNA-sequencing (scRNA-seq).

Single cell transcriptomics (i.e. scRNA-seq) is a next-generation sequencing approach that simultaneously measures the messenger RNA concentrations (encoded by DNA/genome/genetic blueprint) of thousands of genes, in individual cells. This enables researchers to gain a high-resolution view of cells to unravel heterogenous cell populations and better understand individual cell functions in the body. Although several single-cell protocols exist, the sequencing has traditionally been performed using Illumina technology and sequencing platforms.”

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Thank you for reading!

If all the talk about P-values has you concerned, learn Estimation Stats

P-values got you down? Move past them.

Below are the first couple of paragraphs from an exceptionally clear and helpful webpage that provides an introduction to estimation statistics. P-values are a simple, useful metric from a time when computation was labor intensive but we live in a more data-rich world now and it’s now possible to do statistics in a way that captures the parts we care about—like effect size.

ESTIMATION STATS / WHAT IS ESTIMATION STATS?

This site provides you with a web application to plot experimental data from an estimation statistics perspective. You may have found significance testing and P-values problematic; you may be asking what comes next.

Introducing Estimation Statistics

Estimation statistics is a simple framework that—while avoiding the pitfalls of significance testing—uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one's experiment/intervention, as opposed to significance testing.

Significance testing calculates the probability (the P value) that the experimental data would be observed, if the intervention did not produce a change in the metric measured (i.e. the null hypothesis). This leads analysts to apply a false dichotomy on the experimental intervention.

Estimation statistics, on the other hand, focuses on the magnitude of the effect (the effect size) and its precision. This encourages analysts to gain a deeper understanding of the metrics used, and how they relate to the natural processes being studied.

https://www.estimationstats.com/#/background

What your genome won't tolerate

This is one of those projects that’s so clearly interesting and important that it’s surprising nobody has done it already: specifically, this is a very thorough and well-executed analysis of all the places in the human genome that do not appear to tolerate being mutated. If you have access, it’s worth reading. —RPR

Measuring intolerance to mutation in human genetics

Zachary L. Fuller, Jeremy J. Berg, Hakhamanesh Mostafavi, Guy Sella & Molly Przeworski

Nature Genetics (Research Article)

Abstract—In numerous applications, from working with animal models to mapping the genetic basis of human disease susceptibility, knowing whether a single disrupting mutation in a gene is likely to be deleterious is useful. With this goal in mind, a number of measures have been developed to identify genes in which protein-truncating variants (PTVs), or other types of mutations, are absent or kept at very low frequency in large population samples—genes that appear ‘intolerant’ to mutation. One measure in particular, the probability of being loss-of-function intolerant (pLI), has been widely adopted. This measure was designed to classify genes into three categories, null, recessive and haploinsufficient, on the basis of the contrast between observed and expected numbers of PTVs. Such population-genetic approaches can be useful in many applications. As we clarify, however, they reflect the strength of selection acting on heterozygotes and not dominance or haploinsufficiency.

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Finding Ancient Ancestors in Modern DNA

Digging ancient signals out of modern human genomes

With new genome analysis tools, scientists have made significant advances in our understanding of modern humans' origins and ancient migrations.

But trying to find ancient DNA, let alone prove that the ancient DNA is ancestral to a population living today is extremely challenging.

A new study in Molecular Biology and Evolution (MBE) adds to this understanding by reconstructing artificial genomes with the analyses of the genome of 565 contemporary South Asian individuals to extract ancient signals that recapitulate the long history of human migration and admixture in the region.

"All in all, our results provide a proof-of-principle for the feasibility of retrieving ancient genetic signals from contemporary human subjects, as if they were genomes from the past embedded in amber," said Luca Pagani, the research coordinator of the study.

The study was led by Burak Yelmen and Mayukh Mondal from the Institute of Genomics of the University of Tartu, Estonia and coordinated by Luca Pagani from the same institution and from the University of Padova, Italy.

"The genetic components we managed to extract from modern genomes are invaluable, given the shortage of ancient DNA available from South Asian human remains, and allow us to elucidate the genetic composition of the ancient populations that inhabited the area," said Burak Yelmen, co-first author of the study.

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Chemotherapy and Immune Response, Complex Therapeutic Terrain

Chemotherapy-Stimulated Immune Response: An Open Debate

Jonathan Goodman, Cancer Therapy Advisor

“A recent review suggested that chemotherapy may prime cancer to respond to checkpoint inhibition.1 According to the review, which was published in the Annals of Oncology earlier this year, this may occur for a variety of reasons, depending primarily on the mechanism of action of the chemotherapy in question. 

In the past, these predictions may have been surprising to researchers in oncology, as chemotherapy was previously thought to be immunosuppressive. Yet, the authors argue, the effects of chemotherapy can “induce favorable immunogenic conditions within the tumor microenvironment, which may be difficult to achieve by just targeting immune cells.” 

In this setting, chemotherapy functions as the first part of a 2-stage evolutionary trap, where in the first stage clinicians actively select for a tumor microenvironment in which checkpoint blockade is most likely to be effective. With cyclophosphamide, for example, immunogenic cell death may be induced, and the drug may lead to dendritic cell homeostasis.2,3 Both are favorable immunomodulatory effects that may lead to an improved immune response —especially, it appears, when checkpoint blockade is used. 

A recent editorial published in the Annals of Oncology, however, suggests that the notion of turning “cold” tumors “hot” may be a misconception.4 This, according to a study author, Thomas Helleday, PhD, professor of translational oncology and director of the Sheffield Cancer Centre at the University of Sheffield, England, is for several key reasons, each of which has to do with the selective processes caused by chemotherapeutics.“

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