A Multi-labeled Tree Edit Distance for Comparing "Clonal Trees" of Tumor Progression

We introduce a new edit distance measure between a pair of "clonal trees", each representing the progression and mutational heterogeneity of a tumor sample, constructed by the use of single cell or bulk high throughput sequencing data. In a clonal tree, each vertex represents a specific tumor clone, and is labeled with one or more mutations in a way that each mutation is assigned to the oldest clone that harbors it. Given two clonal trees, our multi-labeled tree edit distance (MLTED) measure is defined as the minimum number of mutation/label deletions, (empty) leaf deletions, and vertex (clonal) expansions, applied in any order, to convert each of the two trees to the maximal common tree. We show that the MLTED measure can be computed efficiently in polynomial time and it captures the similarity between trees of different clonal granularity well. We have implemented our algorithm to compute MLTED exactly and applied it to a variety of data sets successfully. The source code of our method can be found in: https://github.com/khaled-rahman/leafDelTED

A Multi-Labeled Tree Dissimilarity Measure for Comparing “Clonal Trees” of Tumor Progression

We introduce a new dissimilarity measure between a pair of “clonal trees”, each representing the progression and mutational heterogeneity of a tumor sample, constructed by the use of single cell or bulk high throughput sequencing data. In a clonal tree, each vertex represents a specific tumor clone, and is labeled with one or more mutations in a way that each mutation is assigned to the oldest clone that harbors it. Given two clonal trees, our multi-labeled tree dissimilarity (MLTD) measure is defined as the minimum number of mutation/label deletions, (empty) leaf deletions, and vertex (clonal) expansions, applied in any order, to convert each of the two trees to the maximum common tree. We show that the MLTD measure can be computed efficiently in polynomial time and it captures the similarity between trees of different clonal granularity well

Cadmium toxicity in nature generates the cancerous problems

A naturally occurring metal, cadmium is noticed in tiny amounts in a variety of sources such as food, water, soil, and the atmosphere. Cadmium may be edified in all soils and rocks, including coal and mineral fertilizers. In general, non-smoker people are getting affected by food contamination. The main theme of this paper is cancer activity by cadmium pollution of our environment. Foods are getting contaminated by industrial wastes. Wastes have Cd2+ ions which can bind with plant materials and also bind with animal muscles. On the other hand, the smoker people take the cadmium by smoking tobacco. The cadmium can easily bind up with the organs like the lung, prostate, breast, bone, etc. When industrial wastes are thrown into rivers, ponds, or open spaces, particularly in South Asian nations, the water becomes poisoned and the soil becomes deteriorated; as a result, the environment becomes more hazardous. In consequence, people can be oppressed by significant sicknesses that occurred by Cd2+, such as osteoporosis, renal dysfunction, anemia, alveolar malignancy, and also other conditions

CRISPRpred: A flexible and efficient tool for sgRNAs on-target activity prediction in CRISPR/Cas9 systems

<div><p>The CRISPR/Cas9-sgRNA system has recently become a popular tool for genome editing and a very hot topic in the field of medical research. In this system, Cas9 protein is directed to a desired location for gene engineering and cleaves target DNA sequence which is complementary to a 20-nucleotide guide sequence found within the sgRNA. A lot of experimental efforts, ranging from <i>in vivo</i> selection to <i>in silico</i> modeling, have been made for efficient designing of sgRNAs in CRISPR/Cas9 system. In this article, we present a novel tool, called CRISPRpred, for efficient <i>in silico</i> prediction of sgRNAs on-target activity which is based on the applications of Support Vector Machine (SVM) model. To conduct experiments, we have used a benchmark dataset of 17 genes and 5310 guide sequences where there are only 20% true values. CRISPRpred achieves Area Under Receiver Operating Characteristics Curve (AUROC-Curve), Area Under Precision Recall Curve (AUPR-Curve) and maximum Matthews Correlation Coefficient (MCC) as 0.85, 0.56 and 0.48, respectively. Our tool shows approximately 5% improvement in AUPR-Curve and after analyzing all evaluation metrics, we find that CRISPRpred is better than the current state-of-the-art. CRISPRpred is enough flexible to extract relevant features and use them in a learning algorithm. The source code of our entire software with relevant dataset can be found in the following link: <a href="https://github.com/khaled-buet/CRISPRpred" target="_blank">https://github.com/khaled-buet/CRISPRpred</a>.</p></div