Depicting Epistemological Obstacles in Understanding the Concept of Sequence and Series

This study aimed to discover epistemological obstacle on secondary students to solve sequence and series problems based on three indicators, there are a conceptual obstacle, procedural obstacle, and operational technique obstacle. This study was descriptive with qualitative research approaches. Data were collected with the test and interview method. The subjects in this study are students of SMP Negeri 86 Jakarta class VIII based on the errors seen from the diagnostic tests that had been tested. The analysis was done by giving written tests which are essay and interview formatted. Results on analysis showed that: (1) Conceptual obstacle, obstacle that was experienced by students are: students considered that a pattern was said as a numeral pattern because they own odd numeral pattern and own 2,2,2 of difference; were not able to find exact pattern within the problem; considering that Fibonacci numeral sequence was a pattern that form prime numeral pattern; were not able to differ the concept of arithmetics and geometry sequence; were not able to understand the concept of first quarter on arithmetics sequence; error when interpreted the meaning of problems; were not able to intepret what was given on mathematics model; interpreting sum of the first 20 quarters with sequences which own the 20th quarter; and interpreting sum of the first 20 quarters with the 20th quarter; (2) While on procedural obstacle, obstacle that was experienced are: interpreting numeral pattern if they own their pair; error on determining multiplication or difference; and applying formulas incorrectly; (3) Last on operasional technique obstacle, obstacle that was experienced are error on calculation and using sign and symbol mathematics incorrectly.

MicroSEC: Sequence error filtering pipeline for formalin-fixed and paraffin-embedded samples

The clinical sequencing of tumors is usually performed on formalin-fixed, paraffin-embedded (FFPE) samples and results in many sequencing errors. Most of these errors are detected in chimeric reads caused by single-strand DNA molecules with microhomology. Our filtering pipeline, MicroSEC, focuses on the uneven distribution of mutations in each read and removes the sequencing errors in FFPE samples without eliminating the true mutations that are also detected in fresh frozen samples.

Low cost DNA data storage using photolithographic synthesis and advanced information reconstruction and error correction

Due to its longevity and enormous information density, DNA is an attractive medium for archival storage. The current hamstring of DNA data storage systems—both in cost and speed—is synthesis. The key idea for breaking this bottleneck pursued in this work is to move beyond the low-error and expensive synthesis employed almost exclusively in today’s systems, towards cheaper, potentially faster, but high-error synthesis technologies. Here, we demonstrate a DNA storage system that relies on massively parallel light-directed synthesis, which is considerably cheaper than conventional solid-phase synthesis. However, this technology has a high sequence error rate when optimized for speed. We demonstrate that even in this high-error regime, reliable storage of information is possible, by developing a pipeline of algorithms for encoding and reconstruction of the information. In our experiments, we store a file containing sheet music of Mozart, and show perfect data recovery from low synthesis fidelity DNA.

Author Correction: Ultrasensitive amplicon barcoding for next-generation sequencing facilitating sequence error and amplification-bias correction

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Ultrasensitive amplicon barcoding for next-generation sequencing facilitating sequence error and amplification-bias correction

The ability to accurately characterize DNA variant proportions using PCR amplification is key to many genetic studies, including studying tumor heterogeneity, 16S microbiome, viral and immune receptor sequencing. We develop a novel generalizable ultrasensitive amplicon barcoding approach that significantly reduces the inflation/deflation of DNA variant proportions due to PCR amplification biases and sequencing errors. This method was applied to immune receptor sequencing, where it significantly improves the quality and estimation of diversity of the resulting library.

Gencore: an efficient tool to generate consensus reads for error suppressing and duplicate removing of NGS data

Background Removing duplicates might be considered as a well-resolved problem in next-generation sequencing (NGS) data processing domain. However, as NGS technology gains more recognition in clinical application, researchers start to pay more attention to its sequencing errors, and prefer to remove these errors while performing deduplication operations. Recently, a new technology called unique molecular identifier (UMI) has been developed to better identify sequencing reads derived from different DNA fragments. Most existing duplicate removing tools cannot handle the UMI-integrated data. Some modern tools can work with UMIs, but are usually slow and use too much memory. Furthermore, existing tools rarely report rich statistical results, which are very important for quality control and downstream analysis. These unmet requirements drove us to develop an ultra-fast, simple, little-weighted but powerful tool for duplicate removing and sequence error suppressing, with features of handling UMIs and reporting informative results. Results This paper presents an efficient tool gencore for duplicate removing and sequence error suppressing of NGS data. This tool clusters the mapped sequencing reads and merges reads in each cluster to generate one single consensus read. While the consensus read is generated, the random errors introduced by library construction and sequencing can be removed. This error-suppressing feature makes gencore very suitable for the application of detecting ultra-low frequency mutations from deep sequencing data. When unique molecular identifier (UMI) technology is applied, gencore can use them to identify the reads derived from same original DNA fragment. Gencore reports statistical results in both HTML and JSON formats. The HTML format report contains many interactive figures plotting statistical coverage and duplication information. The JSON format report contains all the statistical results, and is interpretable for downstream programs. Conclusions Comparing to the conventional tools like Picard and SAMtools, gencore greatly reduces the output data’s mapping mismatches, which are mostly caused by errors. Comparing to some new tools like UMI-Reducer and UMI-tools, gencore runs much faster, uses less memory, generates better consensus reads and provides simpler interfaces. To our best knowledge, gencore is the only duplicate removing tool that generates both informative HTML and JSON reports. This tool is available at: https://github.com/OpenGene/gencore

gencore: An Efficient Tool to Generate Consensus Reads for Error Suppressing and Duplicate Removing of NGS data

BackgroundRemoving duplicates might be considered as a well-resolved problem in next-generation sequencing (NGS) data processing domain. However, as NGS technology gains more recognition in clinical applications (i.e. cancer testing), researchers start to pay more attention to its sequencing errors, and prefer to remove these errors while performing deduplication operations. Recently, a new technology called unique molecular identifier (UMI) has been developed to better identify sequencing reads derived from different DNA fragments. Most existing duplicate removing tools cannot handle the UMI-integrated data. Some modern tools can work with UMIs, but are usually slow and use too much memory, making them not suitable for cloud-based deployment. Furthermore, existing tools rarely report rich statistical results, which are very important for quality control and downstream analysis. These unmet requirements drove us to develop an ultra-fast, simple, little-weighted but powerful tool for duplicate removing and sequence error suppressing, with features of handling UMIs and reporting informative results.ResultsThis paper presents an efficient tool gencore for duplicate removing and sequence error suppressing of NGS data. This tool clusters the mapped sequencing reads and merges reads in each cluster to generate one single consensus read. While the consensus read is generated, the random errors introduced by library construction and sequencing can be removed. This error-suppressing feature makes gencore very suitable for the application of detecting ultra-low frequency mutations from deep sequencing data. When unique molecular identifier (UMI) technology is applied, gencore can use them to identify the reads derived from same original DNA fragment. gencore reports statistical results in both HTML and JSON formats. The HTML format report contains many interactive figures plotting statistical coverage and duplication information. The JSON format report contains all the statistical results, and is interpretable for downstream programs.ConclusionsComparing to the conventional tools like Picard and SAMtools, gencore greatly reduces the output data’s mapping mismatches, which are mostly caused by errors. Comparing to some new tools like UMI-Reducer and UMI-tools, gencore runs much faster, uses less memory, generates better consensus reads and provides simpler interfaces. To our best knowledge, gencore is the only duplicate removing tool that generates both informative HTML and JSON reports. This tool is available at: https://github.com/OpenGene/gencore