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Primer: Generative models from NLP for sequence data. Abstract: Generative models are powerful tools for capturing functional constraints within families of biological sequences. Autoregressive models, developed in natural language processing and related fields, provide a useful approach to modeling sequence data without imposing a rigid alignment structure on the data. In this primer, we will review the math and intuition behind these models, survey advancements in model parameterization, and compare strategies for sampling from the models to generate new sequences.

Finally, we will discuss important considerations when applying these models to biological data. Debora Marks. HMS Systems Biology. Abstract: I will describe a set of machine learning methods for protein design with a focus on accelerating antibody discovery for specificity and affinity. I will also motivate these methods for other applications more broadly in genomics.

Antibodies and nanobodies are highly valued molecular tools, used in research for isolating and imaging specific proteins, and in medical applications as therapeutics. However, for a large number of human and model-organism proteins, existing antibodies are non-existent or unreliable. Emerging experimental techniques enable orders-of-magnitude improvement in the number of sequences assayed for target affinity but are notoriously non-specific and not always well-folded. We have explored the use of generative probabilistic models for this design challenge.

We found that the high heterogeneity in antibody sequence length poses a fundamental problem for existing methods, and instead exploited model architectures from natural language processing to develop "alignment-free" predictions.

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We also developed strategies for designing highly diversified libraries based on these models. Finally, we trained these models not just on assayed sequences but on standing evolutionary diversity, taking full advantage of the experiments already done by nature. Small pilot studies were successful, and we have now generated a library with hundreds of thousands of sequences, which is being evaluated experimentally by our collaborators.

May 29, Peter Koo. Eddy Lab, Harvard. Interpretable deep learning models for biological sequence analysis. Abstract: Deep learning methods have the potential to make a significant impact in biology and healthcare, but a major challenge is understanding the reasons behind their predictions. In the first part, I will present results from interrogating a convolutional neural network CNN trained to infer sequence specificities of RNA-binding proteins.

We find that in addition to sequence motifs, our CNN learns a model that considers the number of motifs, their spacing, and both positive and negative effects of RNA structure context. In the second part, I will discuss ongoing research which demonstrates how deep learning can help design better models for protein contact predictions. Specifically, we interpret a variational autoencoder VAE that is trained on aligned, homologous protein sequences. We find that our VAEs capture phylogenetic relationships with an approximate Bayesian mixture model of profiles, i.

Fall Schedule:. July 11, David Knowles Standford Med. Transcriptomic modeling of chemotherapy side effects using human iPSC-derived cardiomyocytes. We investigate using a panel of such cell lines to understand the genetic basis of interindividual differences in response to a specific chemotherapy drug, doxorubicin. Anthracycline-induced cardiotoxicity ACT is a key limiting factor in setting optimal chemotherapy regimes, with almost half of patients expected to develop congestive heart failure given high doses.

However, the genetic basis of sensitivity to anthracyclines remains unclear. We created a panel of human iPSC-derived cardiomyocytes from 45 individuals and performed RNA-seq after 24h exposure to varying doxorubicin dosages. The transcriptomic response is substantial: the majority of genes are differentially expressed and over genes show evidence of differential splicing, the later driven by reduced splicing fidelity in the presence of doxorubicin.

We show that inter-individual variation in transcriptional response is predictive of in vitro cell damage, which in turn is associated with in vivo ACT risk. We developed an efficient linear mixed model, suez, which detects response-expression quantitative trait loci QTLs.

These molecular response QTLs are enriched for lower p-values in ACT genome-wide association and enable prediction of cellular damage, supporting the in vivo relevance of our map of genetic regulation of cellular response to anthracyclines. September 19, Mapping the brain with machine learning. The complexity and sheer number of neurons make it impractical to map a brain by hand, but an automated approach is increasingly possible. In this talk, I will present an overview of algorithms for tracing individual neurons and their connectivity from microscope images of brain tissue.

In particular, I will discuss deep learning methods for image segmentation, the kinds of errors such algorithms make, and approaches for fixing these errors without human intervention. Primer: Why is deep learning so deep? In this primer, we will show mathematically why both of these statements are true.

Specifically, we will see that depth leads to an exponentially greater ability to express even simple polynomial functions. We will identify why some initializations and architectures impede learning in deeper networks, and demonstrate both theoretically and empirically several principles to bear in mind when designing a deep neural network that will learn effectively.

September 26, Yaron Singer Harvard CS. Maximizing submodular functions exponentially faster. The algorithms are designed for submodular function maximization which is the algorithmic engine behind applications such as clustering, network analysis, feature selection, Bayesian inference, ranking, speech and document summarization, recommendation systems, hyperparameter tuning, and many others. Since applications of submodular functions are ubiquitous across machine learning and data sets become larger, there is consistent demand for accelerating submodular maximization algorithms.

Adam Breuer Harvard Gov. Primer: Submodular maximization and machine learning. These functions capture a key property that is common to many problems: we experience diminishing returns as we select additional items or features, clusters, nodes, keyphrases, etc. In this talk, we will survey submodular functions in a variety of salient applications and show why maximizing such functions is challenging.

We will then describe simple approximation algorithms with provably optimal guarantees and glimpse into cutting edge research. October 10, Biomedical data sharing and analysis with privacy.


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Building upon modern cryptographic tools, we introduce privacy-preserving computational protocols that could encourage data sharing and collaboration in biomedicine. First, we describe the first scalable and secure protocol for large-scale genome-wide association analysis that facilitates quality control and population stratification correction while maintaining the confidentiality of underlying genotypes and phenotypes.

We show the protocol could feasibly scale to a million individuals. Second, we introduce a protocol for securely training a neural network model of drug-target interaction DTI that ensures the confidentiality of all underlying drugs, targets, and observed interactions. Our protocol scales to a real dataset of more than a million interactions, and is more accurate than state-of-the-art DTI prediction methods.

Using our protocol, we discover novel DTIs that we experimentally validated via targeted assays. Our work lays a foundation for more effective and cooperative biomedical research. October 17, Manifold learning yields insight into cellular state space under complex experimental conditions. While these technologies hold great potential for improving our understanding of cellular state space, they also pose new challenges in terms of scale, complexity, noise, measurement artifact which require advanced mathematical and algorithmic tools to extract underlying biological signals.

Further as experimental designs become more complex, there are multiple samples patients or conditions under which single-cell RNA sequencing datasets are generated and must be batch corrected and the corresponding populations of single cells compared. In this talk, I cover one of most promising techniques to tackle these problems: manifold learning. Manifold learning provides a powerful structure for algorithmic approaches to denoise the data, visualize the data and understand progressions, clusters and other regulatory patterns, as well as correct for batch effects to unify data.

I will cover two alternative approaches to manifold learning, graph signal processing GSP and deep learning DL , and show results in several projects including: 1 MAGIC Markov Affinity-based Graph Imputation of Cells : an algorithm that low-pass filters data after learning a data graph, for denoising and transcript recover of single cells, validated on HMLE breast cancer cells undergoing an epithelial-to-mesenchymal transition. We find that SAUCIE performs all the above tasks efficiently and can further be used for stratifying patients themselves on the basis of their single cell populations.

Finally, I will preview ongoing work in neural network architectures for predicting dynamics and other biological tasks. Primer: Manifold learning and graph signal processing of high-dimensional, high-throughput biological data. We will also introduce graph signal processing and the general concept of treating measurements as signals on a cell-cell graph. We will show the utility of this view in our techniques such as MAGIC markov affinity-based graph imputation of cells for data denoising and imputation, and MELD manifold-enhancement of latent dimensions for enhancing latent experimental signals and performing causal inference on drivers of experimental differences.

October 24, Brian Cleary Regev and Lander labs, Broad. Studying cell and tissue physiology with random composite experiments. Specifically, it has the potential to transform work in two distinct fields, histology and genetics, using a common approach: composite experiments. With these approaches, methods that can today image genes in a sample will be leveraged to measure thousands of genes, and genetic perturbation studies can be scaled by 1,, fold. I will describe both the theoretical underpinnings of these approaches, as well as the status of ongoing efforts to implement composite experiments in the lab.

Lightning Talk Social. A central innovation is the development of a new class of Bayesian tree models for data that arise from continuous evolution along a latent nonparametric tree. We have recently identified five key genetic pathways impacting T2D risk, and I am interested in whether we can use these pathways along with other clinical data to improve the classification and ultimately management of patients with T2D. Brian Trippe , Broderick Lab, MIT CSB: Generalized linear models and Bayesian inference provide a powerful toolkit for building interpretable models with coherent quantification of uncertainty, but are often computationally expensive to use on high-dimensional datasets.

We present an approximation method which enables more efficient, accurate inference with theoretical guarantees on quality. Eli Weinstein , Marks Lab, Harvard Biophysics: The massive increase in genetic sequence data from diverse, uncultured microorganisms offers opportunities for the discovery of novel and useful molecular systems. I'll describe computational methods for finding genetic loci that are modular or programmable; our approach does not depend on identifying homology to previously characterized systems, relying instead on inference of sequence models and statistical tests for diversity.

October 31, Experimental design for maximizing cell type discovery in single-cell RNA-seq data. Here, inspired by bandit ideas, we show a novel application to iterative experimental design in multi-tissue single-cell RNA-seq scRNA-seq data. Given a budget and modeling cell type information across tissues, they both estimate how many cells are required for sampling from each tissue with the goal of maximizing cell type discovery across samples from multiple iterations.

In both real and simulated data, we demonstrate the advantages these algorithms provide in data collection planning when compared to a random strategy in the absence of experimental design. Primer: Robust nonlinear manifold learning for single cell RNA-seq data. We present a nonlinear latent variable model with robust, heavy-tail error modeling and adaptive kernel learning to capture low dimensional nonlinear structure in scRNA-seq data.

We model residual errors with a heavy-tailed Student's t-distribution to control for observed technical and biological noise. We compare our approach to common dimension reduction tools to highlight our model's ability to enable important downstream tasks, including clustering and inferring cell developmental trajectories, on available experimental data.

We show that our robust nonlinear manifold is well suited for raw, unfiltered gene counts from high throughput sequencing technologies for visualization and exploration of cell states. November 07, Topic modeling the transcriptional spectrum in innate lymphoid cells. Yet, these cell types may share important biological signals and have been observed in some contexts to essentially continuously span a functional spectrum.

In single-cell RNA-seq data from skin-resident ILCs, we observed a multi-dimensional spectrum of ILCs that was shifted and functionally reconfigured by induction of psoriasis. To capture and explore these fluid, mixed transcriptional states, we used topic modeling by latent Dirichlet allocation LDA , a method covered in the great primer David will give! Topic weights captured relationships not well described by clusters and, through their functional interpretation, enabled a more nuanced view of similarities among cells.

Using experimental techniques in a mouse model, we validated several computational predictions, including the previously undescribed presence of quiescent-like tissue-resident ILCs and differentiation of activated skin-resident ILC2s into pathological ILC3s. Approaches like topic modeling should be valuable in representing other continuous cell states and in uncovering dynamic cellular activation in response to a stimulus.

Primer: Intro to topic models. We will discuss why LDA works and ways to elaborate upon it. Finally, we will survey applications of LDA in biology. November 14, Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq. Here, I will discuss analysis of real and simulated single-cell data showing that matrix factorization can yield components corresponding to cell types and cellular activities such as life-cycle processes or responses to environmental stimuli.

However, one limitation of many matrix factorizations is that their stochastic optimization algorithms can yield variable solutions when run multiple times on the same dataset which reduces the interpretability of the result. To address this limitation, we developed a meta-analysis approach that we call consensus matrix factorization which averages over multiple replicates to increase the robustness of the solution. We show with simulated data that, in particular, the consensus implementation of NMF cNMF outperforms several other factorizations at inferring cell-type and activity programs, including the relative contribution of programs in each cell.

Applied to published brain organoid and visual cortex single-cell RNA-Seq datasets, cNMF refines the hierarchy of cell types and identifies both expected e. We make cNMF available to the community and illustrate how this approach can provide key insights into gene expression variation within and between cell types. Aleksandrina Goeva Macosko Lab. Primer: Intro to non-negative matrix factorization. While singular value decomposition SVD, PCA is optimal with respect to minimizing data movement, the resulting features are often not interpretable or robust across experiments.

Non-negative matrix factorization NMF is a powerful alternative that may be applied when the data is non-negative e. In this primer, we will formulate an NMF objective function and optimization algorithm, paying special attention to practical challenges that Dylan will explore in the main talk. And time permitting, we will survey familiar probabilistic models built on NMF, such as the topic models from last week.

December 5, Functional mapping can be achieved by densely tiling single guide RNAs sgRNAs across a non-coding region of interest, where each sgRNA enables linking of a unique genomic location to an observable phenotype. Luca Pinello will open up the talk by motivating why people are excited about CRISPR tiling screen and describing the key ideas and challenges.

Single-cell trajectory reconstruction, exploration and mapping from omics data. Several methods have been developed for reconstructing developmental trajectories from single-cell transcriptomic data, but efforts on analyzing single-cell epigenomic data and on trajectory visualization remain limited. Here we present STREAM, an interactive pipeline capable of disentangling and visualizing complex branching trajectories from both single-cell transcriptomic and epigenomic data.

First, Luca Pinello will set the stage presenting the basic concepts of how to build a trajectory inference approach from scratch a cookbook perspective. Then Huidong Chen will describe the method behind STREAM - a novel Elastic Principal Graph implementation ElPiGraph , followed by a detailed discussion of how to visualize the learned trajectory and how to discover branch-specific genes, or genes differentiating between trajectory branches.

We will close off with examining what we have learned so far and what the future directions and challenges are. December 12, Lucas Janson Harvard Statistics. To address this practical problem, we propose a new framework of model-X knockoffs, which acts as a wrapper around any arbitrarily complex, e.

Our method relies only on a model for the explanatory variables X, and in fact makes no assumptions at all about the response variable's distribution. To our knowledge, no other procedure solves the FDR-controlled variable selection problem in such generality, but in the restricted settings where competitors exist we demonstrate the superior power of knockoffs through simulations. Wenshuo Wang Harvard Statistics.

As data sets become more complex, the number of candidate features is quickly growing and very often even exceeds the number of observations we can afford to collect. This brings huge challenges for statisticians and scientists, as traditional variable selection methods fail in these cases. This talk reviews these challenges and existing statistical methods to address them. We will discuss the advantages and disadvantages of those methods, ultimately motivating the novel approach presented in the main talk.

February 7, Rapid bacterial adaptation within individual human microbiomes. The degree to which individual commensal species functionally change within individual people has remained elusive, as it is difficult to identify de novo mutations from metagenomic data of mixed communities.

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We have recently discovered that commensal members of our microbiota acquire de novo mutations with strong fitness consequences within individual people. In this talk, I will discuss the challenges with identifying within-person evolution from metagenomics alone, describe how culture-dependent methods enable powerful evolutionary inferences, and touch on the implications of an evolving microbiome for data interpretation and therapy.

February 14, AI for health needs causality. However, many of the most important problems, such as predicting disease progression, personalizing treatment to the individual, drug discovery, and finding optimal treatment policies, all require a fundamentally different way of thinking. Motivated by these challenges, my lab has been developing several new approaches for causal inference from observational data. Primer on causal inference.

If smokers are observed to have higher rates of lung cancer, should we legislate to discourage smoking? Such a policy will only be effective if smoking itself is the cause of cancer and the correlation between cancer rate and smoking is not explained by other factors, such as lifestyle choices.

Problems like these are well described in the language of causal inference. In this primer, we explain the difference between statistical and causal reasoning, and introduce the notions of confounding, causal graphs and counterfactuals. We cover the problem of estimating causal effects from experimental and observational data, as well as sufficient assumptions to make causal statements based on statistical quantities.

February 21, Leveraging long range phasing to detect mosaicism in blood at ultra-low allelic fractions. One answer we welcome others!

In this talk, we will describe how we phased the UK Biobank to chromosome-scale accuracy [3,4], developed HMM-based machinery to sensitively call mosaic alterations, and probed the data to reveal new insights into the causes and consequences of clonal hematopoiesis. February 28, Linking gut microbiomes, genomes and phenotypes via linear mixed models and kernel methods.

However, the associations between microbiome, our genome, our environment and our health are not well understood. Our approaches combine linear mixed models — the statistical backbone of GWAS and phenotype prediction methods — with common techniques from statistical ecology, and with kernel regression approaches from machine learning. In the first part of the talk, I will describe approaches to investigate the role of host genetics in shaping the gut microbiome. In the second part, I will describe approaches to investigate how host genetics and the microbiome interact with traits such as obesity and glucose levels.

I will show that the fraction of phenotypic variance explained by the microbiome is often comparable to that of host genetics, which provides a positive outlook towards microbiome-based therapeutics of metabolic disorders. It has recently been accepted for publication in Nature. Primer: Kernel Methods and the Kernel "Trick". Kernel methods allow us to apply some of our familiar linear tools to nonlinear and structured data, using similarities between data points as the basis for classification, regression, and other analyses like PCA.

March 7, Jacob Oppenheim Indigo Agriculture. The spread of mass sequencing has revealed the inability of the Linnean system both to identify organisms and to capture the variation between them. Identification with a sequence allows for the explicit modeling of distance between organisms and ordination of the resulting phylogenetic distance space. We study the use of a common topic model, LDA Latent Dirichlet Allocation , to capture the differences between sequences.

We show that distance in the latent space of topics reproduces alignment distance between closely related taxa. Additionally, we find that the dimensions of this space reflect the hierarchy of biological relationships. This transformation allows for fast comparison of taxa and gaussian process modeling of the properties of unsequenced strains and phenotypic interpolation based on their neighbors.

These results represent a comprehensive and extensible methodology for the modeling of biological diversity. March 21, Evolutionary dynamics on any population structure. Population structure, which can be represented as a graph or network, affects which traits evolve. Understanding evolutionary game dynamics in heterogeneously structured populations is difficult. For arbitrary selection intensity, the problem is in a computational complexity class which suggests there is no efficient algorithm. I will present recently published work that provides a solution for weak selection, which applies to any graph or social network.

The method uses coalescent theory and relies on calculating the meeting times of random walks. The method is used to evaluate large numbers of diverse and heterogeneous population structures for their propensity to favor cooperation. I will demonstrate how small changes in population structuregraph surgeryaffect evolutionary outcomes.

It turns out that cooperation flourishes most in societies that are based on strong pairwise ties. March 28, Samuel Friedman Data Sciences Platform. Convolutions over these tensors learn to detect motifs useful for variant filtering and calling. Variant filtering models learn to classify variants as artifact or real.

Variant calling models learn to segment genomic positions into the diploid genotypes. We will demonstrate how these models can integrate summary statistic information for faster training and potential applications in unsupervised learning. We will also explore several hyper-parameter optimization strategies for architecture selection.

Improvements in both sensitivity and precision with respect to current state-of-the-art filtration methods like gaussian mixture models, random forests, and deep variant will be presented. April 4, Multitask learning approaches to biological network inference: linking model estimation across diverse related datasets. We developed a framework to reconstruct gene regulatory networks from expression datasets generated in separate studies — and thus, because of technical variation different dates, handlers, laboratories, protocols etc… , challenging to integrate.

In this talk, I will introduce how we currently learn regulatory networks from gene expression data, and then, how we extend our methods to learn multiple networks from related datasets jointly through multitask learning. In particular, our method aims to be able to detect weaker patterns that are conserved across datasets, while also being able to detect dataset-unique interactions. In addition, adaptive penalties may be used to favor models that include interactions derived from multiple sources of prior knowledge including orthogonal genomics experiments.

Using two unicellular model organisms, we show that joint network inference outperforms inference from a single dataset. Finally, we also demonstrate that our method is robust to false edges in the prior and to low condition overlap across datasets. Because of the increasing practice of data sharing in Biology, we speculate that cross-study inference methods will be largely valuable in the near future, increasing our ability to learn more robust and generalizable hypotheses and concepts.

Primer: Inference of biological networks with biophysically motivated methods. This talk will focus on enumerating the elements of computational strategies that, when coupled to appropriate experimental designs, can lead to accurate large-scale models of chromatin-state and transcriptional regulatory structure and dynamics.

We highlight four research questions that require further investigation in order to make progress in network inference: using overall constraints on network structure like sparsity, use of informative priors and data integration to constrain individual model parameters, estimation of latent regulatory factor activity under varying cell conditions, and new methods for learning and modeling regulatory factor interactions.

We conclude with examples of applying this strategy to: 1 human and mouse lymphocyte development and function and 2 inference from single-cell and spacial transcriptomics aimed at healthy and diseased brain and spinal tissues. April 11, Learning protein structure with a differentiable simulator. Standard methodology involves two steps: 1 defining an energy landscape, whether with physics, statistics, or homology, and 2 sampling low-energy conformations. We have been developing an alternative approach to bridge this gap by directly training energy landscapes in tandem with the conformational sampling algorithms that operate on them.

April 25, Evolutionary Dynamics. The three fundamental principles of evolution are mutation, selection and cooperation. I will present the mathematical formalism of evolution focussing on stochastic processes. I will discuss amplifiers and suppressors of natural selection, evolutionary game theory and evolutionary graph theory.

Primer: Hamilton's rule makes no prediction and cannot be tested empirically. It is often perceived as a statement that makes predictions about natural selection in situations where interactions occur between genetic relatives. It turns out that this view is incorrect. A simple mathematical analysis reveals that "exact and general'' formulation of Hamilton's rule, which is widely endorsed by its proponents, is not a consequence of natural selection and not even a statement specifically about biology.

Instead it is a relationship among slopes of linear regression that holds for any suitable data set. It follows that the general form of Hamilton's rule makes no predictions and cannot be tested empirically. May 2, How to make a picture worth a thousand numbers: models and methods in biological image analysis. In this talk, we will present approaches we've been developing to create a new generation of these tools and methods. May 9, AI audit to uncover blind spots of data.

I will discuss the simple idea of AI audit, where we leverage the predictive power of machine learning to systematically perform quality control of various components of the data pipeline. I will illustrate this framework with three diverse examples: integrating single-cell RNA-Seqs, designing new proteins, and word embeddings. Primer: Contrastive PCA. Widely-used techniques such as principal component analysis PCA aim to identify dominant trends in one dataset. However, in many settings we have datasets collected in different conditions, e.

We propose a new method, contrastive principal component analysis cPCA , which identifies low-dimensional structures that are enriched in a dataset relative to comparison data. In a wide variety of experiments, we demonstrate that cPCA with a background dataset enables us to visualize dataset-specific patterns missed by PCA and other standard methods. We further provide a geometric interpretation of cPCA and strong mathematical guarantees.

An implementation of cPCA is publicly available, and can be used for exploratory data analysis in applications where PCA is currently used. May 16, Interpreting Sequence Models. Interpreting these models is a challenge. I will discuss some of the methods we have applied to understanding sequence-based models, in particular, what we can learn with minimal understanding of the internals of the model itself. Primer: How philosophy of science can help us better deploy machine learning in biology. Will new efforts in machine learning only enable new predictive capabilities or could these tools contribute to new ways of observing and reasoning about biological systems?

This primer explores concepts from philosophy of science that can help to orient researchers and evaluate how machine learning may add explanatory power to models of biology in addition to improving prediction. May 23, Convolutional models of molecular structure. In particular, a number of studies over the last year have made a convincing case for the use of CNNs within the field of molecular modelling.

As an example of these recent developments, we will present our work on using convolutions to predict mutation-induced changes-of-stability ddgs in proteins. We will demonstrate how a simple convolutional model using a purely data-driven approach achieves performance comparable to that of state-of-the-art methods in the field.

Finally, we will discuss current theoretical developments in the area of convolutions, including the quest for rotational equivariance. Primer: Learning from molecular structure. In particular, neural networks have been used extensively, with successful applications in for instance the prediction of secondary structure, aggregation propensities, and disorder.

In contrast, the 3D structure of molecules has been modelled almost exclusively with carefully parameterised physical force fields, which are notoriously difficult to optimise from data. Recent developments in Machine Learning are changing this picture, making it possible to learn structure-sequence relationships directly from raw molecular structures. In this primer, we will briefly review these developments, and introduce the concept of convolutional neural networks, which form the basis for many of the current activities, including the work we will present as our main talk.

September 6, David Kelley Calico Labs. Reading the rules of gene regulation from the human noncoding genome. In particular, thousands of noncoding loci associated with diseases and physical traits lack mechanistic explanation. I'll present a machine-learning system to predict cell type-specific epigenetic and transcriptional profiles in large mammalian genomes from DNA sequence alone.

Using convolutional neural networks, this system identifies promoters and distal regulatory elements and synthesizes their content to make effective gene expression predictions. I'll show that model predictions for the influence of genomic variants on gene expression align well to causal variants underlying eQTLs in human populations and can be useful for generating mechanistic hypotheses to enable GWAS loci fine mapping.

Primer: Classifying genomic sequences with convolutional neural networks. This primer will trace some of the history of neural networks with an eye towards the practical lessons learnt along the way. Then building on the idea of the Position Weight Matrix as a motif detector we will explore exactly what convolution means when applied to a DNA sequence. While drawing examples from computer vision and natural language processing, our focus will be on the application of CNNs to genomic data. Special cation combinations can maintain high primer annealing specificity over a broad range of annealing temperatures.

This eliminates the need for optimization of annealing temperatures for each individual primer—template system and also allows the use of non-ideal PCR assays with different primer annealing temperatures. The optimal primer annealing temperature is dependent on the base composition i. Magnesium ions are a critical DNA polymerase cofactor necessary for enzyme activity. It is claimed that these reagents relieve secondary DNA structure e. Guidelines for degenerate primer design and use.

PCR primer sequences are often deduced from amino acid sequences if the exact nucleotide sequence of their target is unknown. However, because of the degeneracy of the genetic code, the deduced sequences may vary at one or more positions. A common solution in these cases is to use a degenerate primer, which is a mixture of similar primers that have different bases at the variable positions. Using degenerate primers can lead to difficulties optimizing PCR assays: within a degenerate primer mixture only a limited number of primer molecules are complementary to the template; the melting temperature T m of primer sequences may vary significantly; and the sequences of some primers can be complementary to those of others.

For these reasons, amplification conditions are required that minimize nonspecific primer—template and primer—primer interactions. The following guidance may help when designing and using degenerate primers. Amplification of PCR products longer than 3—4 kb is often compromised by nonspecific primer annealing, suboptimal cycling conditions, and secondary structures in the DNA template.

Lengthy optimization is often necessary, by varying factors such as cycling conditions, primer and dNTP concentrations, and special additives. While depurination is usually not a problem in standard PCR, it can significantly influence the amplification of longer PCR fragments. This is because longer templates are proportionally more depurinated than shorter ones. For this reason, very short denaturation steps of only 10 seconds give higher yields and no background smearing compared to denaturation steps of 30 seconds or 1 minute which leads to PCR failure; see figure Effect of cycling conditions.

Extensive depurination is also observed during the final extension step. Secondary structures such as hairpin loops, which are often caused by GC-rich template stretches, interfere with efficient amplification of long PCR products. This problem can be overcome by adding reagents that modify the melting behavior of DNA to help resolve secondary structures at lower temperatures. Once a mismatch occurs during synthesis, Taq DNA polymerase will either extend the mismatched strand or fall off the template strand, leading to mutated or incomplete PCR products, respectively.

Proofreading DNA polymerases contain an inherent 3' to 5' exonuclease activity that removes base-pair mismatches. The robustness of this enzyme allows its use in many different PCR assays. However, as this enzyme is active at room temperature, it is necessary to perform reaction setup on ice to avoid nonspecific amplification.

With an average error rate of 1 in 10, nucleotides, Taq DNA polymerase and its variants are less accurate than thermostable enzymes of DNA polymerase family B. However, due to its versatility, Taq DNA polymerase is still the enzyme of choice for most routine applications and when used with a stringent hot-start, is suitable for several challenging PCR applications.

When amplification reaction setup is performed at room temperature, primers can bind nonspecifically to each other, forming primer—dimers. During amplification cycles, primer—dimers can be extended to produce nonspecific products, which reduces specific product yield. To produce hot-start DNA polymerases, Taq DNA polymerase activity can be inhibited at lower temperatures with antibodies or, more effectively, with chemical modifiers that form covalent bonds with amino acids in the polymerase.

The chemical modification leads to complete inactivation of the polymerase until the covalent bonds are broken during the initial heat activation step. In contrast, in antibody-mediated hot-start procedures, antibodies bind to the polymerase by relatively weak non-covalent forces, which leaves some polymerase molecules in their active state. This sometimes leads to nonspecific primer extension products that can be further amplified during PCR. These products appear as smearing or incorrectly sized fragments when run on an agarose gel.

High-fidelity PCR enzymes are ideally suited to applications requiring a low error rate, such as cloning, sequencing, and site-directed mutagenesis. However, if the enzyme is not provided in a hot-start version, the 3' to 5' exonuclease activity can degrade primers during PCR setup and the early stages of PCR.

Type I error vs Type II error

Nonspecific priming caused by shortened primers can result in smearing on a gel or amplification failure — especially when using low amounts of template. It should be noted that the proofreading function often causes high-fidelity enzymes to work more slowly than other DNA polymerases. In addition, the A-addition function required for direct UA- or TA-cloning is strongly reduced, resulting in the need for blunt-end cloning with lower ligation and transformation efficiency.

In theory, each PCR cycle doubles the amount of amplicon in the reaction. Each PCR cycle consists of template denaturation, primer annealing and primer extension. If the temperatures for annealing and extension are similar, these two processes can be combined. Each stage of the cycle must be optimized in terms of time and temperature for each template and primer pair combination.

After the required number of cycles has been completed see table Guidelines for determining the number of PCR cycles for further information , the amplified product may be analyzed or used in downstream applications. Basic terms used in data analysis are given below.

Type 1, type 2, type S, and type M errors

For more information on data analysis, refer to the recommendations from the manufacturer of your real-time cycler. Data are displayed as sigmoidal-shaped amplification plots when using a linear scale , in which the fluorescence is plotted against the number of cycles see figure Typical amplification plot. Before levels of nucleic acid target can be quantified in real-time PCR, the raw data must be analyzed and baseline and threshold values set.

When different probes are used in a single experiment e. Furthermore, analysis of different PCR products from a single experiment using SYBR Green detection requires baseline and threshold adjustments for each individual assay. Baseline : The baseline is the noise level in early cycles, typically measured between cycles 3 and 15, where there is no detectable increase in fluorescence due to amplification products.

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The number of cycles used to calculate the baseline can be changed and should be reduced if high template amounts are used or if the expression level of the target gene is high see figure Baseline and threshold settings. To set the baseline, view the fluorescence data in the linear scale amplification plot. Set the baseline so that growth of the amplification plot begins at a cycle number greater than the highest baseline cycle number.

The baseline needs to be set individually for each target sequence. The average fluorescence value detected within the early cycles is subtracted from the fluorescence value obtained from amplification products. Recent versions of software for various real-time cyclers allow automatic, optimized baseline settings for individual samples. Background : This refers to nonspecific fluorescence in the reaction, for example, due to inefficient quenching of the fluorophore or the presence of large amounts of double-stranded DNA template when using SYBR Green.

The background component of the signal is mathematically removed by the software algorithm of the real-time cycler. Normalized reporter signal Rn : This is the emission intensity of the reporter dye divided by the emission intensity of the passive reference dye measured in each cycle. Passive reference dye : On some real-time cyclers, the fluorescent dye ROX serves as an internal reference for normalization of the fluorescent signal. It allows correction of well-to-well variation due to pipetting inaccuracies, well position, and fluorescence fluctuations.

Threshold : The threshold is adjusted to a value above the background and significantly below the plateau of an amplification plot. It must be placed within the linear region of the amplification curve, which represents the detectable log-linear range of the PCR. The threshold value should be set within the logarithmic amplification plot view to enable easy identification of the log-linear phase of the PCR.

If several targets are used in the real-time experiment, the threshold must be set for each target. Threshold cycle C T or crossing point Cp : The cycle at which the amplification plot crosses the threshold i. C T can be a fractional number and allows calculation of the starting template amount. Endogenous reference gene : This is a gene whose expression level should not differ between samples, such as a housekeeping gene 3.

The exact amount of template in the reaction is not determined. An endogenous reference gene corrects for possible RNA degradation or presence of inhibitors in the RNA sample, and for variation in RNA content, reverse-transcription efficiency, nucleic acid recovery, and sample handling. For selection of the optimal reference gene s , algorithms have been developed which allow the choice of the optimal reference, dependent on the experimental set-up 4. Internal control : This is a control sequence that is amplified in the same reaction as the target sequence and detected with a different probe i.

An internal control is often used to rule out failure of amplification in cases where the target sequence is not detected. Calibrator sample : This is a reference sample used in relative quantification e. The calibrator sample can be any sample, but is usually a control e. Positive control : This is a control reaction using a known amount of template.

A positive control is usually used to check that the primer set or primer—probe set works and that the reaction has been set up correctly. No template control NTC : This is a control reaction that contains all essential components of the amplification reaction except the template. This enables detection of contamination due to contaminated reagents or foreign DNA, e. DNA contamination can be detected by performing a no RT control reaction in which no reverse transcriptase is added.

Standard : This is a sample of known concentration or copy number used to construct a standard curve. The standard curve is commonly generated using a dilution series of at least 5 different concentrations of the standard.

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Each standard curve should be checked for validity, with the value for the slope falling between —3. Standards are ideally measured in triplicate for each concentration. Standards which give a slope differing greatly from these values should be discarded. Efficiency and slope : The slope of a standard curve provides an indication of the efficiency of the real-time PCR.

A slope of —3. A slope of less than —3. A slope of greater than —3. This can occur when values are measured in the nonlinear phase of the reaction or it can indicate the presence of inhibitors in the reaction. The efficiency of a real-time PCR assay can be calculated by analyzing a template dilution series, plotting the C T values against the log template amount, and determining the slope of the resulting standard curve. Fluorescence is measured during each cycle, which greatly increases the dynamic range of the reaction, since the amount of fluorescence is proportional to the amount of PCR product.

PCR products can be detected using either fluorescent dyes that bind to double-stranded DNA or fluorescently labeled sequence-specific probes. The excitation and emission maxima of SYBR Green I are at nm and nm, respectively, allowing use of the dye with any real-time cycler. Detection takes place in the extension step of real-time PCR. Signal intensity increases with increasing cycle number due to the accumulation of PCR product. Use of SYBR Green enables analysis of many different targets without having to synthesize target-specific labeled probes.

However, nonspecific PCR products and primer—dimers will also contribute to the fluorescent signal. Fluorescently labeled probes provide a highly sensitive method of detection, as only the desired PCR product is detected. However, PCR specificity is also important when using sequence-specific probes. Amplification artifacts such as nonspecific PCR products and primer—dimers may also be produced, which can result in reduced yields of the desired PCR product. Competition between the specific product and reaction artifacts for reaction components can compromise assay sensitivity and efficiency.

The following probe chemistries are frequently used. TaqMan probes : sequence-specific oligonucleotide probes carrying a fluorophore and a quencher moiety. The fluorophore is attached at the 5' end of the probe and the quencher moiety is located at the 3' end. This results in detectable fluorescence that is proportional to the amount of accumulated PCR product. When the 2 probes bind, their fluorophores come into close proximity, allowing energy transfer from a donor fluorophore to an acceptor fluorophore.

Therefore, fluorescence is detected during the annealing phase of PCR and is proportional to the amount of PCR product. As the FRET system uses 2 primers and 2 probes, good design of the primers and probes is critical for successful results. Dyes used for fluorogenic probes in real-time PCR : For real-time PCR with sequence-specific probes, various fluorescent dyes are available, each with its own excitation and emission maxima see table Dyes commonly used for quantitative, real-time PCR.

The wide variety of dyes makes multiplex, real-time PCR possible detection of 2 or more amplicons in the same reaction , provided the dyes are compatible with the excitation and detection capabilities of the real-time cycler used, and the emission spectra of the chosen dyes are sufficiently distinct from one another. Therefore, when carrying out multiplex PCR, it is best practice to use dyes with the widest channel separation possible to avoid any potential signal crosstalk.

Other probes : Many probe suppliers have developed their own proprietary dyes. For further information, please refer to the web pages of the respective suppliers. Target nucleic acids can be quantified using either absolute quantification or relative quantification. Absolute quantification determines the absolute amount of target expressed as copy number or concentration , whereas relative quantification determines, as the first step of analysis, the ratio between the amount of target and the amount of a control e.

Subsequently, this normalized value can then be used to compare, for example, differential gene expression in different samples. Use of external standards enables the level of a gene to be given as an absolute copy number. For gene expression analysis, the most accurate standards are RNA molecules of known copy number or concentration. Depending on the sequence and structure of the target and the efficiency of reverse transcription, only a proportion of the target RNA in the RNA sample will be reverse transcribed. The use of RNA standards takes into account the variable efficiency of reverse transcription.

The amount of unknown target should fall within the range tested. Amplification of the standard dilution series and of the target sequence is carried out in separate wells. The C T values of the standard samples are determined. Then, the C T value of the unknown sample is compared with the standard curve to determine the amount of target in the unknown sample.

It is important to select an appropriate standard for the type of nucleic acid to be quantified. The copy number or concentration of the nucleic acids used as standards must be known. In addition, standards should have the following features:. RNA standards can be created by cloning part or all of the transcript of interest into a standard cloning vector. Ensure that in vitro transcription of the insert leads to generation of the sense transcript. Furthermore, ensure that the RNA used as a standard does not contain any degradation products or aberrant transcripts by checking that it migrates as a single band in gel or capillary electrophoresis.

After determination of RNA concentration by spectrophotometry, the copy number of standard RNA molecules can be calculated using the following formula:. Advantages of this method are that large amounts of standard can be produced, its identity can be verified by sequencing, and the DNA can easily be quantified by spectrophotometry. Plasmid standards should be linearized upstream or downstream of the target sequence, rather than using supercoiled plasmid for amplification. This is because the amplification efficiency of a linearized plasmid often differs from that of the supercoiled conformation and more closely simulates the amplification efficiency of genomic DNA or cDNA.

After spectrophotometric determination of plasmid DNA concentration, the copy number of standard DNA molecules can be calculated using the following formula:. We recommend including at least 20 bp upstream and downstream of the primer binding sites of the amplicons. Free lung worksheets respiratory system homeschool stuff biology corner urinary resources. In eukaryotic cells, such as animal cells and plant cells, DNA replication occurs in the S phase of interphase during the cell cycle.

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