CLIPPER: an add-on to the Trans-Proteomic Pipeline for the automated analysis of TAILS N-terminomics data

Abstract Data analysis in proteomics is complex and with the extra challenges involved in the interpretation of data from N-terminomics experiments, this can be daunting. Therefore, we have devised a rational pipeline of steps to approach N-terminomics data analysis in a statistically-based and valid manner. We have automated these steps in CLIPPER, an add-on to the Trans-Proteomic Pipeline (TPP). Applying CLIPPER to the analysis of N-terminomics data generated by terminal amine isotopic labeling of substrates (TAILS) enables high confidence peptide to protein assignment, protein N-terminal characterization and annotation, and for protease analysis readily allows protease substrate discovery with high confidence.

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Comparison of manual and automated ventricle segmentation in the maternal immune stimulation rat model of schizophrenia

10.1101/2020.06.10.144022 ◽

2020 ◽

Author(s):

Rebecca Winter ◽

Benson Akinola ◽

Elizabeth Barroeta-Hlusicka ◽

Sebastian Meister ◽

Jens Pietzsch ◽

...

Keyword(s):

Data Analysis ◽

Ex Vivo ◽

Brain Mri ◽

Small Animal ◽

Automated Analysis ◽

Ventricular Volume ◽

Rodent Models ◽

Immune Stimulation ◽

Manual Segmentation ◽

Ventricle Segmentation

AbstractMaternal immune stimulation (MIS) is strongly implicated in the etiology of neuropsychiatric disorders. Magnetic resonance imaging (MRI) studies provide evidence for brain structural abnormalities in rodents following prenatal exposure to MIS. Reported volumetric changes in adult MIS offspring comprise among others larger ventricular volumes, consistent with alterations found in patients with schizophrenia. Linking rodent models of MIS with non-invasive small animal neuroimaging modalities thus represents a powerful tool for the investigation of structural endophenotypes. Traditionally manual segmentation of regions-of-interest, which is laborious and prone to low intra- and inter-rater reliability, was employed for data analysis. Recently automated analysis platforms in rodent disease models are emerging. However, none of these has been found to reliably detect ventricular volume changes in MIS nor directly compared manual and automated data analysis strategies. The present study was thus conducted to establish an automated, structural analysis method focused on lateral ventricle segmentation. It was applied to ex-vivo rat brain MRI scans. Performance was validated for phenotype induction following MIS and preventive treatment data and compared to manual segmentation. In conclusion, we present an automated analysis platform to investigate ventricular volume alterations in rodent models thereby encouraging their preclinical use in the search for new urgently needed treatments.

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Cluster Validation

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch038 ◽

2011 ◽

pp. 231-236

Author(s):

Ricardo Vilalta ◽

Tomasz Stepinski

Keyword(s):

Machine Learning ◽

Pattern Recognition ◽

Data Analysis ◽

Visual Inspection ◽

Automated Analysis ◽

Learning Tools ◽

Planetary Surfaces ◽

Martian Surface ◽

New Classification ◽

Domain Experts

Spacecrafts orbiting a selected suite of planets and moons of our solar system are continuously sending long sequences of data back to Earth. The availability of such data provides an opportunity to invoke tools from machine learning and pattern recognition to extract patterns that can help to understand geological processes shaping planetary surfaces. Due to the marked interest of the scientific community on this particular planet, we base our current discussion on Mars, where there are presently three spacecrafts in orbit (e.g., NASA’s Mars Odyssey Orbiter, Mars Reconnaissance Orbiter, ESA’s Mars Express). Despite the abundance of available data describing Martian surface, only a small fraction of the data is being analyzed in detail because current techniques for data analysis of planetary surfaces rely on a simple visual inspection and descriptive characterization of surface landforms (Wilhelms, 1990). The demand for automated analysis of Mars surface has prompted the use of machine learning and pattern recognition tools to generate geomorphic maps, which are thematic maps of landforms (or topographical expressions). Examples of landforms are craters, valley networks, hills, basins, etc. Machine learning can play a vital role in automating the process of geomorphic mapping. A learning system can be employed to either fully automate the process of discovering meaningful landform classes using clustering techniques; or it can be used instead to predict the class of unlabeled landforms (after an expert has manually labeled a representative sample of the landforms) using classification techniques. The impact of these techniques on the analysis of Mars topography can be of immense value due to the sheer size of the Martian surface that remains unmapped. While it is now clear that machine learning can greatly help in automating the detailed analysis of Mars’ surface (Stepinski et al., 2007; Stepinski et al., 2006; Bue and Stepinski, 2006; Stepinski and Vilalta, 2005), an interesting problem, however, arises when an automated data analysis has produced a novel classification of a specific site’s landforms. The problem lies on the interpretation of this new classification as compared to traditionally derived classifications generated through visual inspection by domain experts. Is the new classification novel in all senses? Is the new classification only partially novel, with many landforms matching existing classifications? This article discusses how to assess the value of clusters generated by machine learning tools as applied to the analysis of Mars’ surface.

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Text Analysis Methods for Misinformation–Related Research on Finnish Language Twitter

Future Internet ◽

10.3390/fi13060157 ◽

2021 ◽

Vol 13 (6) ◽

pp. 157

Author(s):

Jari Jussila ◽

Anu Helena Suominen ◽

Atte Partanen ◽

Tapani Honkanen

Keyword(s):

Data Analysis ◽

Automated Analysis ◽

Word Formation ◽

Fake News ◽

Word Count ◽

Twitter Data ◽

Analysis Methods ◽

Twitter Data Analysis ◽

Language Areas ◽

Data Analysis Methods

The dissemination of disinformation and fabricated content on social media is growing. Yet little is known of what the functional Twitter data analysis methods are for languages (such as Finnish) that include word formation with endings and word stems together with derivation and compounding. Furthermore, there is a need to understand which themes linked with misinformation—and the concepts related to it—manifest in different countries and language areas in Twitter discourse. To address this issue, this study explores misinformation and its related concepts: disinformation, fake news, and propaganda in Finnish language tweets. We utilized (1) word cloud clustering, (2) topic modeling, and (3) word count analysis and clustering to detect and analyze misinformation-related concepts and themes connected to those concepts in Finnish language Twitter discussions. Our results are two-fold: (1) those concerning the functional data analysis methods and (2) those about the themes connected in discourse to the misinformation-related concepts. We noticed that each utilized method individually has critical limitations, especially all the automated analysis methods processing for the Finnish language, yet when combined they bring value to the analysis. Moreover, we discovered that politics, both internal and external, are prominent in the Twitter discussions in connection with misinformation and its related concepts of disinformation, fake news, and propaganda.

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Mapping specificity, cleavage entropy, allosteric changes and substrates of blood proteases in a high-throughput screen

Nature Communications ◽

10.1038/s41467-021-21754-8 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Federico Uliana ◽

Matej Vizovišek ◽

Laura Acquasaliente ◽

Rodolfo Ciuffa ◽

Andrea Fossati ◽

...

Keyword(s):

Data Analysis ◽

Analysis Pipeline ◽

Simple Method ◽

Biochemical Processes ◽

Biochemical Assays ◽

Protease Substrate ◽

Data Analysis Pipeline ◽

Selection Of ◽

Physiological Substrates

AbstractProteases are among the largest protein families and critical regulators of biochemical processes like apoptosis and blood coagulation. Knowledge of proteases has been expanded by the development of proteomic approaches, however, technology for multiplexed screening of proteases within native environments is currently lacking behind. Here we introduce a simple method to profile protease activity based on isolation of protease products from native lysates using a 96FASP filter, their analysis in a mass spectrometer and a custom data analysis pipeline. The method is significantly faster, cheaper, technically less demanding, easy to multiplex and produces accurate protease fingerprints. Using the blood cascade proteases as a case study, we obtain protease substrate profiles that can be used to map specificity, cleavage entropy and allosteric effects and to design protease probes. The data further show that protease substrate predictions enable the selection of potential physiological substrates for targeted validation in biochemical assays.

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Data Analysis Strategy for Maximizing High-Confidence Protein Identifications in Complex Proteomes Such as Human Tumor Secretomes and Human Serum

Journal of Proteome Research ◽

10.1021/pr200464c ◽

2011 ◽

Vol 10 (11) ◽

pp. 4993-5005 ◽

Cited By ~ 15

Author(s):

Huan Wang ◽

Hsin-Yao Tang ◽

Glenn C. Tan ◽

David W. Speicher

Keyword(s):

Data Analysis ◽

Human Serum ◽

Human Tumor ◽

High Confidence ◽

Analysis Strategy

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Flexible Data Analysis Pipeline for High-Confidence Proteogenomics

Journal of Proteome Research ◽

10.1021/acs.jproteome.6b00765 ◽

2016 ◽

Vol 15 (12) ◽

pp. 4686-4695 ◽

Cited By ~ 6

Author(s):

Hendrik Weisser ◽

James C. Wright ◽

Jonathan M. Mudge ◽

Petra Gutenbrunner ◽

Jyoti S. Choudhary

Keyword(s):

Data Analysis ◽

High Confidence ◽

Analysis Pipeline ◽

Data Analysis Pipeline

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NILAI-NILAI PENDIDIKAN KARAKTER DALAM KUMPULAN CERITA RAKYAT DAERAH JAMBI KARYA H. ZUKRI NAWAS

Aksara: Jurnal Ilmiah Pendidikan Bahasa dan Sastra Indonesia ◽

10.33087/aksara.v3i2.130 ◽

2020 ◽

Vol 3 (2) ◽

pp. 161

Author(s):

Erlina Zahar ◽

Nurani Lumban Tobing

Keyword(s):

Data Analysis ◽

Hard Work ◽

Mutual Respect ◽

Educational Values ◽

High Confidence ◽

Work Attitude ◽

Good Works

This research aims to describe the educational character values in the collection of Cerita Rakyat Daerah Jambi by H. Zukri Nawas. There 9 aspects of educational character values; namely honesty, bravery, trustworthy, fair, thoughtful, responsible, confident, hardwork, and tolerance aspects. This study included a qualitative type of descriptive. Based on the results of data analysis, it is known that there are 50 quotations which are divided into 9 aspects of educational character as follows: 1) There are 5 quotations of honesty (always talking honestly, working honestly, and admitting any mistakes honestly); 2) there are 11 quotations of bravery aspect (having courage when starting something new and facing evil); 3) there are 5 quotations of trustworthy aspect (have an attitude of trust when carrying out their duties and obligations by exemplify good works; 4) there are 3 quotations of fairness aspect (fair in earning rights under its obligations); 5) there are 4 quotations of thoughtful aspect (have an attitude and right in the act accompanied by a mature thought; 6) there are 7 quotations of being responsible aspect (have a responsibility attitude when doing a job well done); 7) there is 1 quotation of confident aspect (having a high confidence when someone else is undoing it but he still remains at his own); 8) there are 8 quotations of hardworking aspect (have a hard work attitude that always try to get something he wants), and 9) there are 6 quotations of tolerance aspect (have a mutual respect). It can be concluded that there are some educational values found in the collection of Cerita Rakyat Daerah Jambi by H. Zukri Nawas. The aspects are honesty, bravery, trustworthy, fair, thoughtful, responsible, confident, hardwork, and tolerance.

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Machine Learning/Artificial Intelligence Driven Computer Vision for Cuttings Analysis Under Drilling and Completions

10.2118/208509-ms ◽

2021 ◽

Author(s):

Chafaa Badis ◽

Welton Souza ◽

Mohammad Abadullah Yasir ◽

Perminder Sabharwal

Keyword(s):

Computer Vision ◽

Early Stage ◽

Automated Analysis ◽

Hole Size ◽

High Confidence ◽

Large Size ◽

Abnormal Pressure ◽

Potential Problems ◽

Drilling Operations ◽

Shape And Size

Abstract The shape and size of formation cuttings passing through a shaker screen can provide valuable insights about any potential downhole problems. Large size cuttings or carvings may indicate the presence of an abnormal pressure zone and hole size may be enlarged which may lead to NPT events (stuck pipe, loss circulation, etc.), asset loss or HSE incidents. We proposed a new method of real-time automated analysis of cuttings in the shale shaker enabling faster reaction to mitigate risks associated with drilling operations. The solution uses a camera on the shaker screen, capturing the cuttings images and applying computer vision and convolutional neural networks algorithms to identify and classify individual cuttings shape, size and type combined with wireline data to raise alarms on specific conditions and prescribe actions to mitigate the problem. The solution showed a remarkably high confidence in identifying the cutting types and size and in detecting potential problems at their early stage enabling the drilling engineers to take the corrective actions at the onset of an event.

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