Bioinformatics pipeline

Genetic testing is performed to detect changes or variants (also known as SNPs or Single Nucleotide Polymorphisms) in DNA. Pathogenic variants are associated with disease, while benign variants are not. The majority of genetic variants have no known effects on human health, but there may be a few that can cause a disease or symptom.

Our Clinical Genomics service is unique in that it offers comprehensive clinical genomics rather than merely genomics. We generate a "provider-ready" report, intended for individuals or their healthcare providers seeking to find disease-causing variants in genes associated with their disease or symptom. Our service focuses on pathogenic variant discovery, enabling healthcare providers to diagnose diseases and develop treatment, monitoring, and prevention strategies tailored to the patient’s genetic profile.

How accurate is MoodNote Clinical Genomics determination of harmful mutations?

Our algorithm is highly accurate and has been validated. To validate the algorithm, we compared the presence of disease-related genes in about 200 cases of one disease and over 100 cases of another disease. The testing revealed an algorithm sensitivity of 89.6%, specificity of 88.3%, and accuracy of 89.1%.

What does this mean?

Sensitivity refers to how well the algorithm identifies disease-related genes in patients who actually have the disease, while specificity measures how well it avoids falsely identifying these genes in people who don't have the disease. Accuracy reflects the overall reliability of the algorithm in making correct predictions. These high percentages mean the algorithm performs exceptionally well in distinguishing between diseases, ensuring precise and reliable results.

HOW DO WE FIND DISEASE-CAUSING VARIANTS?

A human has about 22,000 genes, each of which may have up to 10,000 variants. Think about discovering a needle in the proverbial haystack! It is no wonder that many users of genomic data, including highly trained medical professionals, become easily overwhelmed by the abundance of data.

American College of Medical Genetics (ACMG) classifies all variants into 5 categories:

Benign
Likely benign
Likely pathogenic
Pathogenic
Uncertain significance or VUS

MoodNote Clinical Genomics-trained staff use sophisticated bioinformatic tools and complex algorithms to discover pathogenic or likely pathogenic variants responsible for disease, and deliver this information in an easy to comprehend report.

In order to identify deleterious variants, we rely on four sets of criteria

1. Clinical significance

Listing of a variant in ClinVar database. If a variant is listed as “pathogenic” or “likely pathogenic” in ClinVar, it is because someone already studied this variant and found that it is indeed associated with a disease or symptom. If a variant listed as “benign” in ClinVar, there is a good chance that this variant is not linked with a particular disease or symptom.

2. Variant frequency in the population

How many people in the general population have a particular variant? Obviously, if most people have that variant, it is unlikely to be associated with a disease since most people in the population are not ill. If a variant is found in less than 5% of the population, there is a good chance that it may be damaging. In other words, the more rare a variant is, the more likely it is associated with a disease.

3. Computational (In silico) predictions

In silico predictors are computer programs that calculate a potential impact of a variant on the protein structure and function. Our bioinformatics analysis factors in calculations and models made by multiple predictors. They include MutationTaster, SIFT, PROVEAN, metaRNN, CADD and others. Our algorithms take into account results of all predictors weigh them with other findings to issue a "verdict" based on ACMG rules.

4. Conservation

If a variant appears in an evolutionary conserved part of the gene, it is more likely to be deleterious. There are several methods to calculate conservation scores. Our algorithms use several conservation metrics including GERP and phastCons. High scores indicate that a variant is more likely to be deleterious.

Report Generation

One critical aspect of our variant discovery process is a thorough literature search. We use sophisticated tools such as UMLS Metathesaurus and other databases to determine whether a gene with a pathogenic variant is indeed associated with the patient's symptoms or condition. This is a challenging task because there are thousands of sources and publications to review. Often, the patient's condition is very rare, with only a few small studies available. Our advanced algorithms automate this process, requiring only minimal human intervention for the final report.

Technical Aspects of Pathogenic variant Discovery

For those interested in the technical aspects of our pathogenic variant discovery process, please see the graphic below depicting a standard workflow of our bioinformatics analysis pipeline.

An infographics depicting pathogenic DNA variant discovery pipeline

BIOINFORMATICS Analysis

Here's a simplified description of the Bioinformatics analysis workflow shown in the image above:

Patient Sample Collection: A sample is collected from the patient, typically a blood, saliva or tissue sample.
Sequencing: The collected sample undergoes sequencing, which reads the DNA to produce raw sequencing data.
Raw Sequencing Data (FASTQ): The raw data from sequencing is stored in FASTQ files, which contain the sequences and their quality scores.
Read Alignment (BAM): The sequences from the FASTQ files are aligned to a reference genome, and the results are stored in BAM files.
Variant Calling (VCF): Differences between the patient's DNA and the reference genome are identified. These differences, called variants, are recorded in VCF files.
Variant Annotation: The identified variants are annotated with additional information, such as their potential effects and relevance to known diseases.
Variant Filtration: The variants are filtered using databases like GnomAD, ClinVar, and PGx data to identify those that are most likely to be significant.
Report Preparation: A report is prepared based on the filtered and annotated variants, including relevant literature searches.
Information to Patient: The final report is communicated to the patient, usually through their healthcare provider, to inform them about the findings and potential implications.

This workflow outlines the steps from collecting a patient's sample to delivering a detailed genomic report that can help in understanding genetic causes of symptoms and guiding medical decisions.

MoodNote Service is not a medical service. Information that is accessible through MoodNote webpages is not intended to diagnose, prevent or treat any disease or condition. MoodNote’s annotation of DNA variants is intended for educational and informational purposes only. Only a trained and duly licensed health care professional, after having conducted a thorough and direct examination, can diagnose or treat an emotional, psychological, psychiatric, neurological or medical condition. If you have any concerns about your health, please contact a qualified health care professional who is duly licensed in your jurisdiction. If you feel suicidal or have a medical emergency, please call 911 (in the US or Canada) or proceed to the nearest emergency room.

MOODNOTE GENOMICS

DISCOVERING PATHOGENIC VARIANTS

HOW DO WE FIND DISEASE-CAUSING VARIANTS?

BIOINFORMATICS Analysis

DISCOVERING PATHOGENIC VARIANTS

HOW DO WE FIND DISEASE-CAUSING VARIANTS?

BIOINFORMATICS Analysis

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