Benchmarking Database Performance for Genomic Data

Khushi, Matloob

doi:10.1002/jcb.25049

Cited by 10 publications

(7 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Primary tables are given an identity which is used as a foreign key in the secondary tables. We believe this work will help to improve the effectiveness of queries using SQL and future benchmarking efforts [39]. Relational schema generated for such linked tables should be beneficial for replying to better and more complex queries.…”

Section: Discussionmentioning

confidence: 99%

A Novel Approach to Data Extraction on Hyperlinked Webpages

2019

View full text Add to dashboard Cite

The World Wide Web has an enormous amount of useful data presented as HTML tables. These tables are often linked to other web pages, providing further detailed information to certain attribute values. Extracting schema of such relational tables is a challenge due to the non-existence of a standard format and a lack of published algorithms. We downloaded 15,000 web pages using our in-house developed web-crawler, from various web sites. Tables from the HTML code were extracted and table rows were labeled with appropriate class labels. Conditional random fields (CRF) were used for the classification of table rows, and a nondeterministic finite automaton (NFA) algorithm was designed to identify simple, complex, hyperlinked, or non-linked tables. A simple schema for non-linked tables was extracted and for the linked-tables, relational schema in the form of primary and foreign-keys (PK and FK) were developed. Child tables were concatenated with the parent table’s attribute value (PK), serving as foreign keys (FKs). Resultantly, these tables could assist with performing better and stronger queries using the join operation. A manual checking of the linked web table results revealed a 99% precision and 68% recall values. Our 15,000-strong downloadable corpus and a novel algorithm will provide the basis for further research in this field.

show abstract

Section: Discussionmentioning

confidence: 99%

A Novel Approach to Data Extraction on Hyperlinked Webpages

2019

View full text Add to dashboard Cite

show abstract

“…MatCol reports colocalisations as the number of objects found to overlap between two channels. This number could be saved in a database which could allow machine learning of large scale analysis among different cell-lines and treatment conditions 26 , 27 . The thresholding method used by MatCol (Equation 1 ) is simple to understand and implement in other tools, if the maintainability of MatCol becomes an issue in the future.…”

Section: Discussionmentioning

confidence: 99%

MatCol: a tool to measure fluorescence signal colocalisation in biological systems

Khushi

Napier

Smyth

et al. 2017

Sci Rep

Self Cite

View full text Add to dashboard Cite

Protein colocalisation is often studied using pixel intensity-based coefficients such as Pearson, Manders, Li or Costes. However, these methods cannot be used to study object-based colocalisations in biological systems. Therefore, a novel method is required to automatically identify regions of fluorescent signal in two channels, identify the co-located parts of these regions, and calculate the statistical significance of the colocalisation. We have developed MatCol to address these needs. MatCol can be used to visualise protein and/or DNA colocalisations and fine tune user-defined parameters for the colocalisation analysis, including the application of median or Wiener filtering to improve the signal to noise ratio. Command-line execution allows batch processing of multiple images. Users can also calculate the statistical significance of the observed object colocalisations compared to overlap by random chance using Student’s t-test. We validated MatCol in a biological setting. The colocalisations of telomeric DNA and TRF2 protein or TRF2 and PML proteins in >350 nuclei derived from three different cell lines revealed a highly significant correlation between manual and MatCol identification of colocalisations (linear regression R2 = 0.81, P < 0.0001). MatCol has the ability to replace manual colocalisation counting, and the potential to be applied to a wide range of biological areas.

show abstract

“…MatQuantify segments an ROI based on its area and can therefore be used to measure 19 structural properties of any organelle at any magnification. The output is saved into a tab-delimited text file, which can be imported into a database for large-scale analysis [ 18 ]. The user needs to know the size of their structure-of-interest, which can be worked out by trial and error.…”

Section: Discussionmentioning

confidence: 99%

Automated classification and characterization of the mitotic spindle following knockdown of a mitosis-related protein

et al. 2017

Self Cite

View full text Add to dashboard Cite

BackgroundCell division (mitosis) results in the equal segregation of chromosomes between two daughter cells. The mitotic spindle plays a pivotal role in chromosome alignment and segregation during metaphase and anaphase. Structural or functional errors of this spindle can cause aneuploidy, a hallmark of many cancers. To investigate if a given protein associates with the mitotic spindle and regulates its assembly, stability, or function, fluorescence microscopy can be performed to determine if disruption of that protein induces phenotypes indicative of spindle dysfunction. Importantly, functional disruption of proteins with specific roles during mitosis can lead to cancer cell death by inducing mitotic insult. However, there is a lack of automated computational tools to detect and quantify the effects of such disruption on spindle integrity.ResultsWe developed the image analysis software tool MatQuantify, which detects both large-scale and subtle structural changes in the spindle or DNA and can be used to statistically compare the effects of different treatments. MatQuantify can quantify various physical properties extracted from fluorescence microscopy images, such as area, lengths of various components, perimeter, eccentricity, fractal dimension, satellite objects and orientation. It can also measure textual properties including entropy, intensities and the standard deviation of intensities. Using MatQuantify, we studied the effect of knocking down the protein clathrin heavy chain (CHC) on the mitotic spindle. We analysed 217 microscopy images of untreated metaphase cells, 172 images of metaphase cells transfected with small interfering RNAs targeting the luciferase gene (as a negative control), and 230 images of metaphase cells depleted of CHC. Using the quantified data, we trained 23 supervised machine learning classification algorithms. The Support Vector Machine learning algorithm was the most accurate method (accuracy: 85.1%; area under the curve: 0.92) for classifying a spindle image. The Kruskal-Wallis and Tukey-Kramer tests demonstrated that solidity, compactness, eccentricity, extent, mean intensity and number of satellite objects (multipolar spindles) significantly differed between CHC-depleted cells and untreated/luciferase-knockdown cells.ConclusionMatQuantify enables automated quantitative analysis of images of mitotic spindles. Using this tool, researchers can unambiguously test if disruption of a protein-of-interest changes metaphase spindle maintenance and thereby affects mitosis.

show abstract

Benchmarking Database Performance for Genomic Data

Cited by 10 publications

References 31 publications

A Novel Approach to Data Extraction on Hyperlinked Webpages

A Novel Approach to Data Extraction on Hyperlinked Webpages

MatCol: a tool to measure fluorescence signal colocalisation in biological systems

Automated classification and characterization of the mitotic spindle following knockdown of a mitosis-related protein

Contact Info

Product

Resources

About