Access python argument inside a Keras custom metric defined on a file different from the launched python script

I’m working on a Machine Learning project with tf.keras, and, to start training, I launch the script via shell as:

python --coeff=2.0 

In, I parse the argument coeff such as:

parser = argparse.ArgumentParser() parser.add_argument("--coeff", type=float, default=1.0) arguments = parser.parse_args() 

In, I defined a tf.keras custom metric, as a function, such as:

def custom_metric(y_true, y_pred): y_true_new = tf.multiply(y_true, MYCOEFF) # .. additional stuff.. 

used by the function load_model (defined in the same file) used to load the model:

def load_model(model_name): model = tf.keras.models.load_model(model_name, custom_objects={'custom_metric': custom_metric}) 

Which is a good and easy way to have MYCOEFF equal to the argument coeff passed as argument when launching the script?

What I tried:

I tried to add, in, the line:

MYCOEFF = arguments.coeff 

and, then, importing it in as:

from file1 import MYCOEFF 

but in my case it doesn’t work, because I obtain an error regarding a “circular import”.

Are there any other (and better) ways?

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Lambda as last layer won’t return predictions for all input data

In my model I have a keras.layers.Lambda layer as the output layer. I use the layer to do some post-processing and return either a 0 or a 1. The problem arises when I run the predict function, the lambda layer only returns a small portion of the last batch data.

Like imagine you have a lambda function that returns the same constant no matter the input given, then the layer won’t give predictions on all data that was passed to it.

For example:

def output_function(x): # Return 1 no matter the input return 1 model = keras.Sequential([ keras.layers.Dense(1, activation='sigmoid'), keras.layers.Lambda(output_function) ]) test_data = tf.random.uniform(shape=(79, 1)) model.predict(test_data, batch_size=32) # Returns an array of only 3 predictions 

I suspect that it is because I don’t return a tensor of the shape (None, 1), but rather just (1). But I don’t know how to rewrite the output_function to return a tensor of shape (None, 1).

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New AI Breast Cancer Model is the First to Show Diagnostic Process

A female doctor looking at mammography scans on her computer.Researchers create a new AI algorithm that can analyze mammography scans, identify whether a lesion is malignant, and show how it reached its conclusion.A female doctor looking at mammography scans on her computer.

A recently developed AI platform is giving medical professionals screening for breast cancer a new, transparent tool for evaluating mammography scans. The research, creates an AI model that evaluates the scans and highlights parts of an image the algorithm finds relevant. The work could help medical professionals determine whether a patient needs an invasive—and often nerve-wracking—biopsy.

“If a computer is going to help make important medical decisions, physicians need to trust that the AI is basing its conclusions on something that makes sense,” Joseph Lo, professor of radiology at Duke and study coauthor said in a press release. “We need algorithms that not only work, but explain themselves and show examples of what they’re basing their conclusions on. That way, whether a physician agrees with the outcome or not, the AI is helping to make better decisions.”

One in every eight women in the US will develop invasive breast cancer during their lifetime. When detected early, a woman has a 93 percent or higher survival rate in the first 5 years. 

Mammography, which uses low-energy X-rays to examine breast tissue for diagnosis and screening, is an effective tool for early detection, but requires a highly skilled radiologist to interpret the scans. However, false negatives and positives do occur, resulting in missed diagnosis and up to 40% of biopsied lesions being benign. 

Using AI for medical imaging analysis has grown significantly in recent years and offers advantages in interpreting data. Implementing AI models also carries risks, especially when an algorithm fails. 

“Our idea was to instead build a system to say that this specific part of a potential cancerous lesion looks a lot like this other one that I’ve seen before,” said study lead and Duke computer science Ph.D. candidate Alina Barnett. “Without these explicit details, medical practitioners will lose time and faith in the system if there’s no way to understand why it sometimes makes mistakes.”

Using 1,136 images from 484 patients within the Duke University Health System, researchers trained the algorithm to locate and evaluate potentially cancerous areas. This was accomplished by training the models to identify unhealthy tissue, or lesions, which often appear as bright or irregular shapes with fuzzy edges on a scan.

Radiologists then labeled these images, teaching the algorithm to focus on the fuzzy edges, also known as margins. Often associated with quick-growing cancerous breast tumor cells, margins are a strong indicator of cancerous lesions. With these carefully labeled images, the AI can compare cancerous and benign edges, and learn to distinguish between them.

The AI model uses the cuDNN-accelerated PyTorch deep learning framework and can be run on two NVIDIA P100 or V100 GPUs.

Figure 1. Top image shows an AI model for spotting pre-cancerous lesions in mammography without revealing the decision-making process. Bottom image shows the IAIA-BL model that tells doctors where it’s looking and how its drawing its conclusions. Credit: Alina Barnett, Duke University.

The researchers found the AI to be as effective as other machine learning-based mammography models, but it holds the advantage of having transparency in its decision-making. When the model is wrong, a radiologist can see how the mistake was made.

According to the study, the model could also be a useful tool when teaching medical students how to read mammogram scans and for resource-constrained areas of the world lacking cancer specialists.

The code from the study is available through GitHub.

Read the study in Nature Machine Intelligence. >>
Read more. >>


Accelerating Medical Image Processing with NVIDIA DALI

Deep learning models require a lot of data to produce accurate predictions. Here’s how to solve the data processing problem for the medical domain with NVIDIA DALI.

Deep learning models require vast amounts of data to produce accurate predictions, and this need becomes more acute every day as models grow in size and complexity. Even large datasets, such as the well-known ImageNet with more than a million images, are not sufficient to achieve state-of-the-art results in modern computer vision tasks.

For this purpose, data augmentation techniques are required to artificially increase the size of a dataset by introducing random disturbances to the data, such as geometric deformations, color transforms, noise addition, and so on. These disturbances help produce models that are more robust in their predictions, avoid overfitting, and deliver better accuracy.

In medical imaging tasks, data augmentation is critical because datasets contain mere hundreds or thousands of samples at best. Models, on the other hand, tend to produce large activations that require a lot of GPU memory, especially when dealing with volumetric data such as CT and MRI scans. This typically results in training with small batch sizes on a small dataset. To avoid overfitting, more elaborate data preprocessing and augmentation techniques are required.

Preprocessing, however, often has a significant impact on the overall performance of the system. This is especially true in applications dealing with large inputs, such as volumetric images. These preprocessing tasks are typically run on the CPU due to simplicity, flexibility, and availability of libraries such as NumPy.

In some applications, such as segmentation or detection in medical images, the GPU utilization during training is usually suboptimal as data preprocessing is usually performed in the CPU. One of the solutions is to attempt to overlap data processing and training fully, but it is not always that simple.

Such a performance bottleneck leads to a chicken and egg problem. Researchers avoid introducing more advanced augmentations into their models due to performance reasons, and libraries don’t put the effort into optimizing preprocessing primitives due to low adoption.

GPU acceleration solution

You can improve the performance of applications with heavy data preprocessing pipelines significantly by offloading data preprocessing to the GPU. The GPU is typically underutilized in such scenarios but can be used to do the work that the CPU cannot complete in time. The result is better hardware utilization, and ultimately faster training.

Just recently, NVIDIA took 3 out of 10 top places in the MICCAI 2021 Brain Tumor Segmentation Challenge, including the winning solution. The winning solution managed to reach a GPU utilization as high as 98% and reduced the total training time by around 5% (30 minutes), by accelerating the preprocessing pipeline of the system (Figure 1).

Training time (time to train for 1000 epochs) with DALI used for preprocessing is 7:20 hours while the native PyTorch data loader is 7:50 hours.
Figure 1. U-Net3D BraTS21 training performance comparison

This difference becomes more significant when you look at the NVIDIA submission for the MLPerf UNet3D benchmark. It used the same network architecture as in the BraTS21 winning solution but with a more complex data loading pipeline and larger input volumes (KITS19 dataset). The performance boost is an impressive 2x end-to-end training speedup when compared with the native pipeline (Figure 2).

Training time (time to train with given accuracy defined in the MLPerf Training benchmark rules) with DALI used for preprocessing is around 80 minutes, with the native PyTorch data loader being 160 minutes. Thanks to DALI, the training is two times shorter.
Figure 2. U-Net3D MLPerf Training 1.1 training performance comparison

This was made possible by NVIDIA Data Loading Library (DALI). DALI provides a set of GPU-accelerated building blocks, enabling you to build a complete data processing pipeline that includes data loading, decoding, and augmentation, and to integrate it with a deep learning framework of choice (Figure 3).

DALI is a tool that handles data loading of various formats: images, video, and audio; decodes, transforms (including augmentation), and provides the data in the tensor native to the deep learning framework format. It uses GPU acceleration if possible for all the mentioned operations.
Figure 3. DALI overview and its usage as a tool for accelerated data loading and preprocessing in DL applications

Volumetric image operations

Originally, DALI was developed as a solution for images classification and detection workflows. Later, it was extended to cover other data domains, such as audio, video, or volumetric images. For more information about volumetric data processing, see 3D Transforms or Numpy Reader.

DALI supports a wide range of image-processing operators. Some can also be applied to volumetric images. Here are some examples worth mentioning:

  • Resize
  • Warp affine
  • Rotate
  • Random object bounding box

To showcase some of the mentioned operations, we use a sample from the BraTS19 dataset, consisting of MRI scans labeled for brain tumor segmentation. Figure 4 shows a two-dimensional slice extracted from a brain MRI scan volume, where the darker region represents a region labeled as an abnormality.

A cut of the brain scan with a region highlighted in a different color that represents an abnormality; a tumor in this case.
Figure 4. A slice from a BraTS19 dataset sample

Resize operator

Resize upscales or downscales the image to a desired shape by interpolating the input pixels. The upscale or downscale is configurable for each dimension separately, including the selection of the interpolation method.

The left image shows a cut of the brain scan with a region highlighted in a different color that represents an abnormality; a tumor in this case. The right image is a recalled cut, where the height dimension is more aggressively scaled than the width.
Figure 5. Reference slice from BraTS19 dataset sample (left) compared with resized sample (right)

Warp affine operator

Warp affine applies a geometric transformation by mapping pixel coordinates from source to destination with a linear transformation.


Warp affine can be used to perform multiple transformations (rotation, flip, shear, scale) in one go.

A cut of the brain scan that is warped: it is stretched from left bottom to the right top and slightly rotated to the right.
Figure 6. A slice of a volume transformed with WarpAffine

Rotate operator

Rotate allows you to rotate a volume around an arbitrary axis, provided as a vector, and an angle. It can also optionally extend the canvas so that the entire rotated image is contained in it. Figure 7 shows an example of a rotated volume.

A cut of the brain scan that is slightly rotated to the left.
Figure 7. A slice from a rotated volume

Random object bounding box operator

Random object bounding box is an operator suited for detection and segmentation tasks. As mentioned earlier, medical datasets tend to be rather small, with target classes (such as abnormalities) occupying a comparatively small area. Furthermore, in many cases the input volume is much larger than the volume expected by the network. If you were to use random cropping windows for training, then the majority would not contain the target. This could cause the training convergence to slow down or bias the network towards false-negative results.

This operator selects pseudo-random crops that can be biased towards sampling a particular label. Connected component analysis is performed on the label map as a pre-step. Then, a connected blob is selected at random, with equal probability. By doing that, the operator avoids overrepresenting larger blobs.

You can also select to restrict the selection to the largest K blobs or specify a minimum blob size. When a particular blob is selected, a random cropping window is generated, within the range containing the given blob. Figure 8 shows this cropping window selection process.

The first image shows three differently colored groups of objects. Then only one, red, is selected, the smaller objects are filtered out. Then only one object is selected and a set of valid cropping windows that includes this object are displayed.
Figure 8. A visualization of the Random object bounding box operation on an artificial 2D image with a set of objects belonging to three different classes (each highlighted with different color)

The gain in learning speed can be significant. On the KITS19 dataset, nnU-Net achieves the same accuracy in 2134 in the test run epochs with the Random object bounding box operator as in 3,222 epochs with random crop.

Typically, the process of finding connected components is slow, but the number of samples in the data set can be small. The operator can be configured to cache the connected component information, so that it’s only calculated during the first epoch of the training.

Accelerate on your own

You can download the latest version of the prebuilt and tested DALI pip packages. The NGC containers for TensorFlow, PyTorch, and MXNet have DALI integrated. You can review the many examples and read the latest release notes for a detailed list of new features and enhancements.

See how DALI can help you accelerate data preprocessing for your deep learning applications. The best place to access is the NVIDIA DALI Documentation, including numerous examples and tutorials. You can also watch our GTC 2021 talk about DALI. DALI is an open-source project, and our code is available on the /NVIDIA/DALI GitHub repo. We welcome your feedback and contributions.


50% Confidence for all bounding boxes in image?

I have trained a model using tf1.15, converted it to tflite and compiled it for the edgetpu, which is all working. However, my confidence values for all bounding boxes is 50%. It seems to recognise what is what somewhat well, such as putting many boxes over and around the object it’s trying to detect, but they’re all 50% confidence so it is difficult to get a clean output. I believe the issue was not caused by any conversions, but during training, as tensorboard also shows this.

I have followed [this]( tutorial for the most part, using mobilenetv2 ssd and my own training data. Initial guess is that my .tfrecord file was made incorrectly, as I did not have .xml files but rather one large .csv.

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Fusing Art and Tech: MORF Gallery CEO Scott Birnbaum on Digital Paintings, NFTs and More

Browse through MORF Gallery — virtually or at an in-person exhibition — and you’ll find robots that paint, digital dreamscape experiences, and fine art brought to life by visual effects. The gallery showcases cutting-edge, one-of-a-kind artwork from award-winning artists who fuse their creative skills with AI, machine learning, robotics and neuroscience. Scott Birnbaum, CEO and Read article >

The post Fusing Art and Tech: MORF Gallery CEO Scott Birnbaum on Digital Paintings, NFTs and More appeared first on The Official NVIDIA Blog.


New NVIDIA AI Enterprise Release Lights Up Data Centers

With a new year underway, NVIDIA is helping enterprises worldwide add modern workloads to their mainstream servers using the latest release of the NVIDIA AI Enterprise software suite. NVIDIA AI Enterprise 1.1 is now generally available. Optimized, certified and supported by NVIDIA, the latest version of the software suite brings new updates including production support Read article >

The post New NVIDIA AI Enterprise Release Lights Up Data Centers appeared first on The Official NVIDIA Blog.


I need a quick answer to a question

How much would it take to train a model which consists of about 2000 pictures on my laptop (I am a beginner but I need to train it for a project) Specs: Ryzen 5 3500u 10gb of ram vega 8 gpu

I was going to do it on the cpu, because I think its more powerful (correct me if I am wrong)

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GANcraft: Turning Gamers into 3D Artists

GANcraft: A hybrid unsupervised neural rendering pipeline for voxel worldsGANcraft is a hybrid neural rendering pipeline to represent large and complex scenes using Minecraft.GANcraft: A hybrid unsupervised neural rendering pipeline for voxel worlds

Scientists at NVIDIA and Cornell University introduced a hybrid unsupervised neural rendering pipeline to represent large and complex scenes efficiently in voxel worlds. Essentially, a 3D artist only needs to build the bare minimum, and the algorithm will do the rest to build a photorealistic world. The researchers applied this hybrid neural rendering pipeline to Minecraft block worlds to generate a far more realistic version of the Minecraft scenery.

Previous works from NVIDIA and the broader research community (pix2pix, pix2pixHD, MUNIT, SPADE) have tackled the problem of image-to-image translation (im2im)—translating an image from one domain to another. At first glance, these methods might seem to offer a simple solution to the task of transforming one world to another—translating one image at a time. However, im2im methods do not preserve viewpoint consistency, as they have no knowledge of the 3D geometry, and each 2D frame is generated independently. As can be seen in the images that follow, the results from these methods produce jitter and abrupt color and texture changes.

MUNIT      SPADE       wc-vid2vid      NSVF-W       GANcraft

 A side by side comparison of past voxel neural rendering pipelines: MUNIT, SPADE, wc-vid2vid, NSVF-W, and GANcraft. You can see the renderings don't hold up as consistently as the GANcraft methodology; blending and distortion occur.
 A side by side comparison of past voxel neural rendering pipelines: MUNIT, SPADE, wc-vid2vid, NSVF-W, and GANcraft. You can see the renderings don't hold up as consistently as the GANcraft methodology; blending and distortion occur.
 A side by side comparison of past voxel neural rendering pipelines: MUNIT, SPADE, wc-vid2vid, NSVF-W, and GANcraft. You can see the renderings don't hold up as consistently as the GANcraft methodology; blending and distortion occur.
 A side by side comparison of past voxel neural rendering pipelines: MUNIT, SPADE, wc-vid2vid, NSVF-W, and GANcraft. You can see the renderings don't hold up as consistently as the GANcraft methodology; blending and distortion occur.
Figure 1. A comparison of prior works and GANcraft.

Enter GANcraft, a new method that directly operates on the 3D input world. 

“As the ground truth photorealistic renderings for a user-created block world simply doesn’t exist, we have to train models with indirect supervision,” the researchers explained in the study

The method works by randomly sampling camera views in the input block world and then imagining what a photorealistic version of that view would look like. This is done with the help of SPADE, prior work from NVIDIA on image-to-image translation, and was the key component in the popular GauGAN demo. GANcraft overcomes the view inconsistency of these generated “pseudo-groundtruths” through the use of a style-conditioning network that can disambiguate the world structure from the rendering style. This enables GANcraft to generate output videos that are view consistent, as well as with different styles as shown in this image!

Figure 2. GANcraft’s methodology enables view consistency in a variety of different styles.

While the results of the research are demonstrated in Minecraft, the method works with other 3D block worlds such as voxels. The potential to shorten the amount of time and expertise needed to build high-definition worlds increases the value of this research. It could help game developers, CGI artists, and the animation industry cut down on the time it takes to build these large and impressive worlds.

If you would like a further breakdown of the potential of this technology, Károly Zsolnai-Fehér highlights the research in his YouTube series: Two Minute Papers:

Figure 3. The YouTube series, Two Minute Papers, covers significant developments in AI as they come onto the scene.

GANcraft was implemented in the Imaginaire library. This library is optimized for the training of generative models and generative adversarial networks, with support for multi-GPU, multi-node, and automatic mixed-precision training. Implementations of over 10 different research works produced by NVIDIA, as well as pretrained models have been released. This library will continue to be updated with newer works over time. 

If you’d like to dive deeper, grab the code on GitHub from the Imaginare repository, see the overview of the framework or read the detailed research paper.

Stay updated on more exciting research from NVIDIA at NVIDIA Research.

The study authors include Zekun Hao, Arun Mallya, Serge Belongie, and Ming-Yu Liu.


Clara Parabricks 3.7 Brings Optimized and Accelerated Workflows for Gene Panels

Up close graphic of two DNA strands.The GPU-accelerated Clara Parabricks v3.7 release brings support for gene panels, RNA-Seq, short tandem repeats, and updates to GATK 4.2 and DeepVariant 1.1.Up close graphic of two DNA strands.

The newest release of GPU-powered NVIDIA Clara Parabricks v3.7 includes updates to germline variant callers, enhanced support for RNA-Seq pipelines, and optimized workflows for gene panels. With now over 50 tools, Clara Parabricks powers accurate and accelerated genomic analysis for gene panels, exomes, and genomes for clinical and research workflows.

To date, Clara Parabricks has demonstrated 60x accelerations for state-of-the-art bioinformatics tools for whole genome workflows (end-to-end analysis in 22 minutes) and exome workflows (end-to-end analysis in 4 minutes), compared to CPU-based environments. Large-scale sequencing projects and other whole genome studies are able to analyze over 60 genomes/day on a single DGX server while both reducing the associated costs and generating more useful insights than ever before.

Many organizations including The National BioBank of Thailand, Human Genome Center and University of Tokyo, Translational Genomics Research Institute, Washington University in St. Louis, Regeneron Genetics Center and the UK Biobank, and Euan Ashley’s lab at Stanford University are using Clara Parabricks for fast genome and exome analysis for large population projects, critically-ill patients, and other cancer and inherited disease projects. Their work aims to accurately and quickly identify disease-causing variants, keeping pace with accelerated next-generation sequencing as well as accelerated genomic analyses.

NVIDIA Clara Parabricks v3.7 overview

  • Accelerate and simplify gene panel workflows with the support of Unique Molecular Identifiers (UMIs) also known as molecular barcodes.
  • RNA-Seq support for transcriptome workflows with second gene fusion caller Arriba and RNA-Seq quantification tool Kallisto.
  • Short tandem repeat (STR) detection with ExpansionHunter.
  • Integration of the latest versions of germline callers DeepVariant v1.1 and GATK v4.2 with HaplotypeCaller.
  • A 10x accelerated BAM2FASTQ tool for converting archived data stored as either BAM or CRAM files back to FASTQ. Datasets can be updated by aligning to new and improved references.
Parabricks workflow diagram.
Figure 1: Clara Parabricks 3.7 includes a UMI workflow for gene panels, ExpansionHunter for short tandem repeats, inclusion of the DeepVariant 1.1 and GATK 4.2 releases, and Kallisto and Arriba for RNA-Seq workflows.

Clara Parabricks 3.7 accelerates and simplifies gene panel analysis 

While whole genome sequencing (WGS) is growing due to large-scale population initiatives, gene panels still dominate clinical genomic analysis. With time being one of the most important factors in clinical care, accelerating and simplifying gene-panel workflows is incredibly important for clinical sequencing centers. By further reducing the analysis bottleneck associated with gene panels, these sequencing centers can return results to clinicians faster, improving the quality of life for their patients. 

Cancer samples used for gene panels are commonly derived from either a solid tumor or consist of cell-free DNA from the blood (liquid biopsies) of a patient. Compared to the discovery work in WGS, gene panels are narrowly focused on identifying genetic variants in known genes that either cause disease or can be targeted with specific therapies.  

Gene panels for inherited diseases are often sequenced to 100x coverage while gene panels in cancer sequencing are sequenced to a much higher depth, up to several 1,000x for liquid biopsy samples. The higher coverage is required to detect lower frequency somatic mutations associated with cancer.

To improve the limit of detection for these gene panels, molecular barcodes or UMIs are used, as they significantly reduce the background noise. This limit of detection is pushed for liquid biopsies, and can include tens of thousands coverage in combination with UMIs to identify those needle-in-the-haystack somatic mutations circulating in the bloodstream. High-depth gene-panel sequencing can reintroduce a computational bottleneck in the required processing of many more sequencing reads. 

With Clara Parabricks, UMI gene panels can now be processed 10x faster than traditional workflows, generating results in less than an hour. 

The analysis workflow is also simplified. From raw FASTQ to consensus FASTQ, a single command line runs multiple inputs compared to the traditional Fulcrum Genomics (Fgbio) equivalent as seen in the example below.

The simplified 3-step workflow of Parabricks compared to the 10-step Fgbio.
Figure 2: Clara Parabricks 3.7 has simplified and accelerated gene panel UMI workflows compared to the Fgbio equivalent.

RNA-Seq support with Arriba and Kallisto

Just as gene panels are important for sequencing cancer analysis, so too are RNA-Seq workflows for transcriptome analysis. In addition to STAR-fusion, Clara Parabricks v3.7 now includes Arriba, a fusion detection algorithm based on the STARRNA-Seq aligner. Gene fusions, in which two distinct genes join due to a large chromosomal alteration, are associated with many different types of cancer from leukemia to solid tumors. 

Arriba can also detect viral integration sites, internal tandem duplications, whole exon duplications, circular RNAs, enhancer hijacking events involving immunoglobulin/T-cell receptor loci, and breakpoints in introns or intergenic regions.

Clara Parabricks v3.7 also incorporates Kallisto, a fast RNA-Seq quantification tool based on pseudo-alignment that identifies transcript abundances (aka gene expression levels based on sequencing read counts) from either bulk or single-cell RNA-Seq datasets. Alignment of RNA-Seq data is the first step of the RNA-Seq analysis workflow. With tools for transcript quantification, read alignment, and fusion calling, Clara Parabricks 3.7 now provides a full suite of tools to support multiple RNA-Seq workflows. 

Short tandem repeat detection with ExpansionHunter

To support genotyping of short tandem repeats (STRs) from short-read sequencing data, ExpansionHunter support has been added to Parabricks v3.7. STRs, also referred to as microsatellites, are ubiquitous in the human genome. These regions of noncoding DNA have accordion-like stretches of DNA containing core repeat units between two and seven nucleotides in length, repeated in tandem, up to several dozen times. 

STRs are extremely useful in applications such as the construction of genetic maps, gene location, genetic linkage analysis, identification of individuals, paternity testing, population genetics, and disease diagnosis. 

There are a number of regions in the human genome consisting of such repeats, which can expand in their number of repetitions, causing disease. Fragile X Syndrome, ALS, and Huntington’s Disease are well-known examples of repeat-associated diseases. 

ExpansionHunter aims to estimate sizes of repeats by performing a targeted search through a BAM/CRAM file for sequencing reads that span, flank, and are fully contained in each STR. 

The addition of somatic callers and support of archived data 

The previous releases of Clara Parabricks in 2021 brought a host of new tools, most importantly the addition of five somatics callers—MuSE, LoFreq, Strelka2, Mutect2, and SomaticSniper—for comprehensive cancer genomic analysis. 

Shows the workflow of Parabricks for somatic workflows and performance benchmarks exceeding native runtimes.
Figure 3: Clara Parabricks v3.6.1 and 3.7 includes five somatic callers for comprehensive accelerated cancer genomic analysis—Muse, LoFreq, Strelka2, Mutect2, and SomaticSniper.

In addition, tools were added to take advantage of archived data in scenarios when original FASTQ files were deleted to save storage space. BAM2FASTQ is an accelerated version of GATK Sam2fastq, which converts an existing BAM or CRAM file to a FASTQ file. This allows users to realign sequencing reads to a new reference genome which will enable more variants to be called, along with providing researchers capabilities to normalize all their data to the same reference. Using the existing fq2bam tool, Clara Parabricks can realign reads from one reference to another in under 2 hours for a 30X whole genome.

Shows the workflow of Parabricks for gremlin workflows and performance benchmarks showing runtime of 22 minutes.
Figure 4: Clara Parabricks v3.7 has added the latest releases of germline variant callers DeepVariant 1.1 and GATK 4.2 with HaplotypeCaller.