215 research outputs found
Integrative single-cell analysis of cardiogenesis identifies developmental trajectories and non-coding mutations in congenital heart disease (Invivo ArchR Object)
No full textThis repo contains invivo ArchR objec
Identification of constrained sequence elements across 239 primate genomes
No full textAbstract Noncoding DNA is central to our understanding of human gene regulation and complex diseases 1,2 , and measuring the evolutionary sequence constraint can establish the functional relevance of putative regulatory elements in the human genome 3–9 . Identifying the genomic elements that have become constrained specifically in primates has been hampered by the faster evolution of noncoding DNA compared to protein-coding DNA 10 , the relatively short timescales separating primate species 11 , and the previously limited availability of whole-genome sequences 12 . Here we construct a whole-genome alignment of 239 species, representing nearly half of all extant species in the primate order. Using this resource, we identified human regulatory elements that are under selective constraint across primates and other mammals at a 5% false discovery rate. We detected 111,318 DNase I hypersensitivity sites and 267,410 transcription factor binding sites that are constrained specifically in primates but not across other placental mammals and validate their cis -regulatory effects on gene expression. These regulatory elements are enriched for human genetic variants that affect gene expression and complex traits and diseases. Our results highlight the important role of recent evolution in regulatory sequence elements differentiating primates, including humans, from other placental mammals
Integrative single-cell analysis of cardiogenesis identifies developmental trajectories and non-coding mutations in congenital heart disease (Invitro ArchR Object)
No full textThis repo contains invitro ArchR objec
Integrative single-cell analysis of cardiogenesis identifies developmental trajectories and non-coding mutations in congenital heart disease (scRNA public datasets reanalysed)
No full textThis repo contains the reanalysed public scRNA dataset
1. In vivo scRNA - human_6_8_12and19_merged_final.rds
2. In vitro scRNA - friedman_final_cleaned.rd
Interplay of polymer and oligonucleotide properties in the nature of antisense effects
No full textAntisense oligonucleotides can be utilized to silence the expression of a target gene via sequence-specific complementary base pairing. Antisense technology is applied as a basic research tool and is being developed therapeutically for a wide range of indications including cancer, inflammatory diseases and viral diseases. Its widespread application is impeded by the poor cellular delivery of oligonucleotides (ONs). Rational design of carriers for enhanced ON delivery demands a better understanding of the role of the vector on the extent and time course of antisense effects. This work highlights the interplay of polymer and ON properties in the nature of polymer mediated antisense responses. First, we demonstrate that ON structure exerts a significant influence on the strength of ON binding to, and dissociation from, the cationic polymer, poly-L-lysine. The finding implicates secondary structure as a relevant design parameter for antisense ONs and stresses the need for a comprehensive evaluation of ON-polymer structure-activity effects. Next, using well-characterized cationic polymer polyethyleneimine (PEI), we focus on understanding the effects of polymer molecular weight (MW) and ON backbone chemistry on antisense activity. We measure physico-chemical properties of complexes between PEI and phosphodiester and phosphorothioate backbone ONs, and evaluate their ability to deliver ONs to cells, leading to an antisense response. Our key finding is that the antisense activity is not determined solely by PEI MW or by ON chemistry, but rather by the interplay of both factors. Of particular importance is the strength of interactions between the carrier and the ON, which determines the rate at which the ONs are delivered intracellularly. Finally, we utilize the chemistry of the ONs as a means to influence the strength of interactions between PEI and ONs, and hence control the final antisense response. We show that it is possible to improve dramatically the efficiency of lower PEI MWs as ON carriers by manipulating the degree of phosphorothioate substitution in the ON chemistry. By correlating the PEI MW & ON chemistry with the observed antisense effects, we draw insightful structure-property relationships that will aid the rational design of ON carriers.Ph.D.Includes bibliographical references (p. 129-137)
Dataset for figures in paper DOI:/10.1016/j.cattod.2015.09.031
No full text<p>Datasets for figures in the publication "</p>
<p>Facile synthesis of palladium phosphide electrocatalysts and their activity for the hydrogen oxidation, hydrogen evolutions, oxygen reduction and formic acid oxidation reactions"</p>
<p>Anthony R.J. Kucernak†, K. Fahy, V. N. Naranammalpuram Sundaram</p>
<p>Department of Chemistry, Imperial College London, London SW7 2AZ, United Kingdom</p>
<p> </p>
<p>† Corresponding author, [email protected], Phone: +44 20 75945831, Fax: +44 20 75945804</p>
<p>Published in Catalysts Today, 2015.</p>
<p>Please cite this paper DOI:/10.1016/j.cattod.2015.09.031 when referencing this data</p>
<p> </p>
Distributed spectrum coordination for multi-radio platform co-existence: an experimental study on the orbit testbed
No full textThis thesis presents an experimental investigation of algorithms for protocol-assisted spectrum coordination of multi-radio platforms in a dense radio environment. With increasing proliferation of new wireless technologies and radio standards such as 802.11b/g, Bluetooth, Zigbee, UWB, WiMax etc, multi-radio devices such as laptop computers, cell phones and PDA's will need to co-exist in shared unlicensed frequency bands.
The common spectrum coordination channel (CSCC) protocol has previously been proposed as a method for nearby devices to exchange spectrum usage and traffic information necessary to execute decentralized co-existence algorithms. This work focuses on the application of CSCC to dense deployments of multi-radio platforms with both 802.11 WLAN and Bluetooth in a typical office/SOHO type environment. Distributed spectrum coordination algorithms listen to these CSCC announcements from radios within range, and back off their transmission parameters to avoid contributing excessive interference. We have developed a set of distributed coordination algorithms, with the objective of achieving efficient co-existence between interfering radios while maintaining acceptable QOS (Quality of Service) at every node.
Specific coordination algorithms considered include Bluetooth defer-transfer (Bo), Simple Source Rate adaptation (Rt), distance based SIR link budget rate adaptation (SIR-BT). Each of these algorithms is defined and evaluated using dual-radio nodes on the 400-node ORBIT radio grid. System performance parameters obtained from the experiments are throughput, file transmission delay (for TCP) and quality of data/audio/video streams (for UDP).
Experimental results are given for a number of device densities and topologies. Significant degradation in throughput and application performance is observed without spectrum coordination. The proposed CSCC-based coordination algorithms are shown to provide significant performance gains, both in terms of system throughput and application level parameters. Overall, for the scenarios considered, the proposed coordination algorithms provide ~50-100% improvement in system throughput when compared to the case with no coordination.M.S.Includes bibliographical references (p. 37-38)
Calibration of the Das, Foresi, Balduzzi and Sundaram three-factor short rate model
No full textThis work is about one specific short-rate model, the Das, Foresi, Balduzzi and Sundaram three-factor short-rate model. The literature states, that this model is able to fit different shapes of rate curves with the three factors contributing in different ways to the shape of the rate curve. We test different numerical methods to calculate the discount factors from the model parameters. Using the best method we fit in the model to different zero rate curves and we can confirm the fact, that the model fits well the different rate curve shapes. We develop a Monte-Carlo based pricing method for this model and we fit the model parameters to different cap volatility curve and to swaption volatility surfaces. Due to the inaccuracy of the pricing function the fits are inaccurate in the case of the swaption surfaces, but in the case of the ATM cap curve we were able to reproduce the shapes of the curves and achieve acceptable fits. Furthermore we present simultaneous fit to the cap curve and to the zero rate curve, and we also propose two methods to fit the model to historical time series. With the fitted parameters and with the same market price of risk functions, which the author used, we fit the parameters of the market price of risk to the current rate curve
Integrative single-cell analysis of cardiogenesis identifies developmental trajectories and non-coding mutations in congenital heart disease (BPNet deep learning DNA sequence models of scATAC-seq data)
No full text==========================
Date: 06/28/2022
Authors: Laksshman Sundaram, Anshul Kundaje
Email: [email protected], [email protected]
==========================
This archive contains deep learning models trained to map DNA sequences to base-resolution pseudo-bulk scATAC-seq profiles from several cell types derived from scATAC-seq profiling of fetal human hearts.
The models are associated with the following preprint/publication
Integrative single-cell analysis of cardiogenesis identifies developmental trajectories and non-coding mutations in congenital heart disease
Mohamed Ameen, Laksshman Sundaram, Abhimanyu Banerjee, Mengcheng Shen, Soumya Kundu, Surag Nair, Anna Shcherbina, Mingxia Gu, Kitchener D. Wilson, Avyay Varadarajan, Nirmal Vadgama, Akshay Balsubramani, Joseph C. Wu, Jesse Engreitz, Kyle Farh, Ioannis Karakikes, Kevin C Wang, Thomas Quertermous, William Greenleaf, Anshul Kundaje
bioRxiv 2022.06.29.498132; doi: https://doi.org/10.1101/2022.06.29.498132
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Directory structure of this archive
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There is one directory for each cell type. There is also a directory for a global pseudobulk model over all cell types.
The file Model_to_cellTypeMapping.txt has a mapping of directory names to the precise cell type names and definitions from the paper. The tab delimited table is reproduced below.
ModelName CellType_mnemonic CellType_FullName
ecm eCM Early cardiomyocytes
acm aCM Atrial cardiomyocytes
vcm vCM Ventricular cardiomyocytes
oft OFT Outflow tract
fb1 FB1 Fibroblast-like cells 1
CFP CFP Cardiac fibroblast progenitors
fb2 FB2 Fibroblast-like cells 2
preCF preCF Pre-cardiac fibroblast
CF CF Cardiac fibroblast
preSMC preSMC Pre-smooth muscle cells
smc SMC Smooth muscle cells
pc PC Pericytes
epc EPC Epicardial cells
nc NC Neural crest
Endo1_2 Endo1_2 Endocardium/Endocardium like cells
lec lEC Lymphatic endothelial cells
aec aEC Arterial endothelial cells
cap Cap Capillaries
vec vEC Venous endothelial cells
pseudobulk Pesudobulk model Pesudobulk model
For each cell type, model were trained on 5 independent folds (numbered 0-4). There are 3 files for each fold:
- ..arch.json : this contains the model architecture in json format
- ..weights.data-00000-of-00001 : this contains the actual model weights
- ..weights.index: this Keras/TensorFlow file contains weights metadata information
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BPNet deep learning models to predict base-resolution, cell-type resolved pseudo-bulk scATAC-seq profiles from DNA sequence
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BPNet is a sequence-to-profile convolutional neural network that uses one-hot-encoded DNA sequence (A=[1,0,0,0], C=[0,1,0,0], G=[0,0,1,0], T=[0,0,0,1]) as input to predict single nucleotide-resolution read count profiles from assays of regulatory activity (Avsec, Weilert, et al. 2021; Trevino et al. 2021). The models take in a sequence context of 2,114 bp around the summit of each ATAC-seq peak and predict cluster-specific scATAC-seq pseudo-bulk Tn5 insertion counts at each base pair for the central 1,000 bp. The BPNet model also uses an input Tn5 bias track which is concatenated to the pre-final layer as explained below. Our BPNet model is a higher capacity version of the architecture introduced in (Avsec, Weilert, et al. 2021). The model architecture consists of 8 dilated residual convolution layers, with 500 filters in each layer. At each layer, the Keras Cropping 1D layer is used to clip out the two edges of the sequence, to match the inputs concatenated to the output of each convolution, which naturally trims the 2,114 bp sequence to a final 1,000 bp profile. Each dilated convolutional layer has a kernel width of 21 and the dilation rate is doubled for every convolutional layer starting at 1. The model predicts the base-resolution 1,000 bp length Tn5 insertion count profile using two complementary outputs: (1) the total Tn5 insertion counts over the 1,000 bp region, and (2) a multinomial probability of Tn5 insertion counts at each position in the 1,000 bp sequence. The predicted (expected) count at a specific position is a multiplication of the predicted total counts and the multinomial probability at that position. To predict the total counts in the 1,000 bp window, the output from the last dilated convolutional layer is passed through a GlobalAveragePooling1D layer in Keras. We estimate the “tn5 bias” for the input sequence using the TOBIAS method (Bentsen et al. 2020). This total bias is concatenated with the output of the pooling layer and passed through a Dense layer with 1 neuron to predict total counts. To predict the per-base logits of the multinomial probability profile output, the output from the last dilated residual convolution is appended with per base TOBIAS “tn5 bias” and passed through a final convolution layer with a single kernel and a kernel width of 1 to predict the per-base logits. BPNet uses a composite loss function consisting of a linear combination of a mean squared error (MSE) loss on the log of the total counts and a multinomial negative log-likelihood loss (MNLL) for the profile probability output. We use a weight of [4.9, 4.3, 18.5, 9.8, 8.9, 4.8, 4.6, 4.9, 12.4, 15.4, 4.3, 6.3, 1.4, 2.6, 7.6, 2.3, 16.3, 7.1 & 3.7] for the MSE loss for clusters c0–c20 (c15-c16 combined as one model), and a weight of 1 for the MNLL loss in the linear combination. The MSE loss weight is derived as the median of total counts across all peak regions for each cluster divided by a factor of 10 (Avsec, Weilert, et al. 2021). We used the ADAM optimizer with early stopping patience of 3 epochs.
A separate BPNet model was trained on pseudobulk scATAC-seq profiles from each scATAC-seq cluster. We used a 5-fold chromosome hold-out cross-validation framework for training, tuning, and test set performance evaluation. The training, evaluation, and test chromosomes used for each fold are as follows.
Test chromosomes:
fold 0: [chr1]
fold 1: [chr19, chr2]
fold 2: [chr3, chr20]
fold 3: [chr13, chr6, chr22]
fold 4: [chr5, chr16]
Validation chromosomes:
fold 0: [chr10, chr8]
fold 1: [chr1]
fold 2: [chr19, chr2]
fold 3: [chr3, chr20]
fold 4: [chr13, chr6, chr22]
For each fold, the the remaining chromosomes that are not in the validation and test set, were used for training.
The model’s performance was evaluated using two different metrics for the two output tasks separately. For the total counts predicted for the 1,000 bp region, the model’s performance is computed with the Spearman correlation of predicted counts to actual counts. The profile prediction performance is evaluated using the Jensen-Shannon Distance, which computes the divergence between two probability distributions; in this case, the observed and predicted base-resolution probability profile over each 1,000 bp region.
For each cell type, BPNet models were trained, tuned, and evaluated on genomic windows consisting of 1 kb scATAC-seq profiles from (1) signal windows centered at summits of scATAC-seq peaks from the cell type and (2) background windows randomly sampled across the genome such that the number of background windows was 10% of the number of signal windows. The selected signal and background windows were further augmented with upto 10 random jitters (+/- 1000 bp).
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Code and data for training models
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Description of all code for this paper is at https://github.com/kundajelab/Cardiogenesis_Repo.
These models were trained using Keras 2.4 and Tensorlow 2.3.0.
The exact code base used to train the models is KerasAC (https://zenodo.org/record/4248179#.X8skj5NKiF0) and it uses seqdataloader (https://zenodo.org/record/3771365#.X8skqZNKiF0) as part of the data processing and model training scripts.
The scATAC-seq peak regions for each cell type are at https://github.com/kundajelab/Cardiogenesis_Repo/tree/main/BPNet/peaksets. All coordinates are with respect to the GRCh38 version of the human genome https://www.encodeproject.org/files/GRCh38_no_alt_analysis_set_GCA_000001405.15/
The BPNet models use a Tn5 bias model for bias correction. For each of the 5 folds, the bias models are available at https://github.com/kundajelab/Cardiogenesis_Repo/tree/main/BPNet/tobias_weights
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How to use models for prediction
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To load a model for a given celltype:
```python
with open("/path/to/models/celltype.arch.json") as f:
m = keras.models.model_from_json(f.read())
m.load_weights("/path/to/models/celltype.weights")
```
Each model takes as input 2114 x 4 one-hot encoded DNA sequence, and has 2 outputs: 1) the profile logits for the central 1000 bp 2) log counts for central 1000 b
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