Sunday, May 28, 2017

RNAseq coursesource materials


Course Source, undergraduate

NIBLSE, bioinformatics core competencies

This set of bioinformatics core competencies for undergraduate life scientists is informed by the survey results of more than 1,200 people, analysis of 90 syllabi addressing bioinformatics across institutions and diverse departments, and discussion among experts across academia and industry. The bulleted lists contain examples illustrating the competencies.

  1. Explain the role of computation and data mining in addressing hypothesis-driven and hypothesis-generating questions within the life sciences: It is crucial for students to have a clear understanding of the role computing and data mining play in the modern life sciences. Given a traditional hypothesis-driven research question, students should have ideas about what types of data and software exist that could help them answer the question quickly and efficiently. They should also appreciate that mining large datasets can generate novel hypotheses to be tested in the lab or field.
    • What hypotheses can one ask based biometric data being compiled (Fitbit, Google, etc.)
    • Understand the role of various databases in identifying potential gene targets for drug development
  2. Summarize key computational concepts, such as algorithms and relational databases, and their applications in the life sciences: In order to make use of sophisticated software and database tools, students must have a basic understanding of the underlying principles that these tools are based upon. Students are not expected to be experts in multiple algorithms or sophisticated databases, but currently the vast majority of life sciences majors never take a programming or database course, and have essentially zero exposure to how these tools work. This must change.
    • Be exposed to how data is organized in relational databases
    • Be able to modify the search parameters to achieve biologically meaningful results
    • Understand underlying algorithm(s) employed in sequence alignment (e.g. BLAST)
  3. Apply statistical concepts used in bioinformatics: Many biology curricula contain statistics, either as a standalone biostatistics course or as part of other courses such as capstone research courses. The primary distinction with regard to bioinformatics has to do with the statistics of large datasets and multiple comparisons.
    • Drug trials: Interpretation of well designed drug trial data
    • Transcriptomics: Understand the statistical modelling used to identify differentially expressed genes; Understand how genes implicated in cancer are identified using panels of sequenced tumor and WT cell lines or biopsies
    • Sequence similarity searching: Understand that there is a probability of finding a given sequence similarity score by chance (the p-value); The size of the database searched affects the probability that they would see that particular score in a particular search (the expectation, or e-value).
  4. Use bioinformatics tools to examine complex biological problems in evolution, information flow, and other important areas of biology: This competency is written broadly so as to encompass a variety of problems addressed using bioinformatics tools, from understanding the evolutionary underpinnings of sequence comparison and homology detection, to the distinctions between genomic sequences, RNA sequences, and protein sequences, to the interpretation of phylogenetic trees. We want to emphasize that bioinformatics tools can be used to teach existing parts of the curriculum such as the central dogma or phylogenetic relationships, thus integrating the bioinformatics into the curriculum as opposed to adding it on as an addition to an already overfull curriculum (and thus forcing decisions about what topic to remove to make room). The point of saying “complex” biological problems is that students should be able to work through a problem with multiple steps, not just perform isolated tasks.
    • Employ gene ontology tools (e.g., Mapman, GO, KEGG).
    • Understand protein sequence, structure, and function, using a variety of tools
    • Understand gene structure, genomic context, alternative splicing using genome browsers
    • Understand concept of homology
  5. Find, retrieve, and organize various types of biological data: Given the numerous and varied datasets currently being generated from all of the ‘omics fields, students should develop the facility to: identify appropriate data repositories; navigate and retrieve data from these databases; and organize data relevant to their area of study (in flat files or small local stand-alone databases).
    • Store and interrogate small datasets using spreadsheets or delimited text files.
    • Navigate and retrieve data from genome browsers
    • Retrieve data from protein and genome databases (PDB, UniProt, NCBI)
  6. Explore and/or model biological interactions, networks and data integration using bioinformatics: Modeling of biological systems at all levels, from cellular to ecological, is being facilitated by technological (e.g., sequencing, biochemical, genetics) and algorithmic advances. These models provide novel insights into the perturbations in systems causative of disease, interactions of microbes with various eukaryotic systems, and how metabolic networks respond to environmental stresses. Students should be familiar with the techniques used to generate these analyses, have the ability to interpret the outputs, and use the data to generate novel hypotheses.
    • Cell Biology: predict impact of gene knockout on cell-signaling pathway
    • Transcriptome: Analysis of transcriptomic data (RNA-Seq) available from SRS using Galaxy
    • Ecological: Analysis of microbial sequence data using QIIME on Galaxy
  7. Use command-line bioinformatics tools and write simple computer scripts: The majority of the datasets students should be familiar with and be able to interact with (e.g., genomic and proteomic sequences, BLAST results, RNASeq and resulting differential expression data) are text files. The most powerful and dynamic way to interact with these datasets is through the command line or shell scripting, both of which are readily acquired skills. Students need to have the flexibility to manipulate their own data, and to create and modify complex data processing and analysis workflows.
    • Write simple unix shell scripts to manipulate files
    • Apply RNASeq analyses using R (STAR, Tophat, DESeq2) to open source data sets (SRS)
    • Build and run statistical analyses using R or Python scripts
    • Run BLAST using command line options
  8. Describe and manage biological data types, structure, and reproducibility: This competency addresses two distinct concerns: 1) each of the varied ‘omics fields produce data in formats particular to its needs, and these formats evolve with changes in technologies and refinements in downstream software; and 2) all experimental data is subject to error and the user must be cognizant of the need to verify the reproducibility of their data. The first concern highlights the requirement for students to develop an awareness of and ability to manipulate different data types given the versioning of formats. The second points to the need for caution, to carry out appropriate statistical analyses on their data as part of normal operating procedures and report the uncertainty of their results, and to provide the relevant information to enable reproduction of their results. Sometimes students have the tendency to assume that anything they retrieve from an online database must be correct; they need to be taught that this is not always the case.
    • Reproducibility: Compare reproducibility of biological replicate data (e.g.transcriptomic data) using statistical tests (Spearman).
    • Formats: Understand the various sequence formats used to store DNA and protein sequences (FASTA, FASTQ); Understand the representation of gene features using Gene Feature Format (GFF) files; Mass-Spec
  9. Interpret the ethical, legal, medical, and social implications of biological data: The increasing scale and penetrance of human genetic and genomic data has greatly enhanced our ability to identify disease-related loci, druggable targets, and potential for gene replacements with developing techniques. However, with this information also comes many ethical, legal, and social questions which are often outpaced by the technological advances. As part of their scientific training, students should debate the medicinal, societal and ethical implications of these information sets and techniques.
    • How does the scientific community protect against the falsification or manipulation of large datasets?
    • Who should have access to this data, and how should it be protected?
    • What are the implications, good and bad, of being able to walk into a doctor’s office and have your genome sequenced and analyzed in minutes?

Friday, May 26, 2017


datacamp, $150 for a year service for advanced courses

raspberryPI can be hooked up to a monitor

Friday, May 19, 2017

day5, jackson lab

Mark Adams
microbial genomics service

mock microbial community => assess DNA extraction method, or other procedures

Aditya Srikanth Kovuri
Sandeep Namburi

NIST, cloud characteristics,
on-demand self-service
broad network access
resource pooling
rapid elasticity,
measured service

Amazon S3

glalaxy cloudman

Google cloud is cheaper than GoogleCloud.
GoogleGenomics API.


Microsoft Azure Research awards

Google Research award

Thursday, May 18, 2017

day4, jackson lab,

=> Krish Karuturi
big data genomics, computational and informatics challenges
TORQUE resource manager

benchmarking pipelines

GSA, Effron & Tibshirani



Peter Robinson, Ph.D., The Jackson Laboratory for Genomic Medicine
Phenotype driven genome analysis

Ontology, disambuilgous terms.

human phenotype ontology

information content (IC) of concept.

semantically similar diseases scores

Washington NL 2009, Plos Biology

Y Ada Zhan, ChIP-seq

bd2kuser@ip-172-31-73-47:~/ChIPseq$ cat readme.txt
# ChIP-seq module #

# ChIP-seq data
In the directory ChIPseq/

# Genome
In the directory ChIPseq/hg38/

# Tools
 fastqc (quality check)
 bowtie (sequence mapping or alignments)
 samtools (manipulating alignments in SAM format. BAM format is a compressed version of SAM file)
 macs2 (peak calling)
 bedtools (to handle sequence coordinate files in BED format)

bd2kuser@ip-172-31-73-47:~/ChIPseq$ cat
# quality check
fastqc GM12878_control_chr1.fastq
fastqc GM12878_CTCF_chr1.fastq

# Prepare genome
bowtie-build hg38/GRCh38.chr1.fa hg38/GRCh38.chr1

# Mapping
bowtie -m 1 -S ./hg38/GRCh38.chr1 GM12878_control_chr1.fastq > GM12878_control_chr1.sam
bowtie -m 1 -S ./hg38/GRCh38.chr1 GM12878_CTCF_chr1.fastq > GM12878_CTCF_chr1.sam

# Further processing
## compress to BAM
samtools view -bSo GM12878_control_chr1.bam GM12878_control_chr1.sam
samtools view -bSo GM12878_CTCF_chr1.bam GM12878_CTCF_chr1.sam
## sort
samtools sort GM12878_control_chr1.bam GM12878_control_chr1.sorted
samtools sort GM12878_CTCF_chr1.bam GM12878_CTCF_chr1.sorted
## index
samtools index GM12878_control_chr1.sorted.bam
samtools index GM12878_CTCF_chr1.sorted.bam

# Peak calling
macs2 callpeak -t GM12878_CTCF_chr1.sorted.bam -c GM12878_control_chr1.sorted.bam -f BAM -g 175000000 -n GM12878_CTCF_chr1 -B -q 0.01

# Check the peak model
Rscript GM12878_CTCF_chr1_model.r

# Motif analysis
## extend summits 100bp on both directions
bedtools slop -i GM12878_CTCF_chr1_summits.bed -g hg38/GRCh38.chr1.size -b 100 > GM12878_CTCF_chr1_summits_ext.bed
## get sequence file (i.e. fasta)
bedtools getfasta -fi hg38/GRCh38.chr1.fa -bed  GM12878_CTCF_chr1_summits_ext.bed -fo GM12878_CTCF_chr1_summits_ext.fa
## The .fa file will be uploaded to MEME online server for motif discovery (

BED file format

MEME motif discovery

ChiPseek website for interactive data analysis,

Wednesday, May 17, 2017

day3, 20170517Wed Jackson Lab, Galaxy, IGV,

=> Paola Vera-Licona
gene network

time series gene expression data -> network

structure-based control of signaling networks (optimization of interaction? )

HER2-positive breast cancer

BiNoM           -> geneXplain --> OCSANA
gene expression -> list TFs ---> mapping pathways + master regulator --> identify optimal combination of intervention from network analysis

candidate genes with p-values
pick largest connected component
using random sampling permutation to evaluate the choice of p-value cutoff.

Using annotated pathway to build a directed nework for intervention analysis and prediction.

How drugble? Drug reposition?


=> Reinhard Laubenbacher

Karl Broman, Reproducible research (should added to my REU bootcamp training).

biostatistics and medical informatics

IGV: need *bam file for alignment, *bai file for index. 

vcf file can be visualized in IGV or Ensembl Variant Effect Predictor.

Usually, large genes tend to have more mutations than small genes. Genes with repetitive elements tend to have more mutations.

network software

=> RTN, bioconductor


=> Cytoscape

=> KENev

=> MARINa (MATlab)

=> ingenuity

=> geneXplain

bioconductor KEGG.db

KEGG.db contains mappings based on older data because the original
  resource was removed from the the public domain before the most
  recent update was produced. This package should now be considered
  deprecated and future versions of Bioconductor may not have it
  available.  Users who want more current data are encouraged to
  look at the KEGGREST or reactome.db packages

KEGG ftp price list

personal account $2000 per year
organization account $5000 per year

Tuesday, May 16, 2017

day 2, afternoon, 20170515 jackson lab

genome data sources

genomes in a bottle

Carl Zimmer

George Church

JAX HPC 256G RAM per node, 20 cores per node,

day2, moring, 20170516

=> Sheng Li, RNAseq
RNAseq library contruction

Kukurba KR, montgomery SB, Cold Spring Harbo Protoc, 2015,

For microRNA, ~20nt, special protocol is required.

stranded and non-stranded library (to distinguish overlapping exons or genes on opposite DNA strands)

minimal reads: 20-25 millions reads  for mammalian transcriptiome

Illumina Hiseq-4000, ~ 4000 millions per lane. 4-8 libraries per lane. Often, double indexing can be used for high number of multiplexing libraries.

2nd step, Gene annotation: GenCode
GTF format

3rd step, gene expression quantification

RNAseq metric,
single-end RPKM, reads per kilobase per million reads
paired-end, FPKM, fragments per kilobase per million reads
nomalize read counts for sequencing depth, length of gene
TPM, transipts per million
 pro: sum of total normalized reads is the same for all samples.(not for  R/FPKM)

before 1st step, Quality check step.
 genebody coverage, (with genes)
 insert sizes
 GC content
 reads distribution
 adaptor enrichment (containmination or PCR amplification bias?)
 read quality

RSeQC, Liguo Wang, Bioinformatics 2012
  polyA selected 3' UTR, so 5'UTR degradation can be a problem.

Public data:
RNA-seq blog

combatR, correct of batch effect

biological degradation of mRNA during aging, using sva latent variable, to distinguish biological degradation from non-biological degradation.

Single cell RNAseq, Ion Mandoiu

psuedotemporal order of cells

single cell mutaional profieing and clonal phylogeny in cancer
Potter, Genome Re

cell type identification in primary visual cortex


challenges in single-cell RNAseq: low RT and sequencing depth, "zero inflated" data


Matching clusters to cell types or organism parts

10X genomics . .
neuron cortex

yeast GEO, aging and large scale

Dang lab
methylation and chip-seq

287 samples, Holstege

  • Sameith K, Amini S, Groot Koerkamp MJ, van Leenen D et al. 
    A high-resolution gene expression atlas of epistasis between gene-specific transcription factors exposes potential mechanisms for genetic interactions. BMC Biol 2015 Dec 23;13:112. PMID: 26700642

Aging, single cell expression data set

physiologically aged hematopoietic stem cells

To uphold appropriate homeostasis of short-lived blood cells, immature blood cells need to proliferate vigorously. Here, using a conditional H2B-mCherry labeling mouse-model, we characterize hematopoietic stem cell (HSC) and progenitor proliferation dynamics in steady state, upon physiological aging and following several types of induced stress. Following transplantation, HSCs shifted towards higher degrees of proliferation that was sustained long-term. HSCs were, by contrast, poorly recruited into proliferation following cytokine-induced mobilization and after acute depletions of selected blood cell lineages. Using indexed single cell sorting coupled to multiplex gene expression analyses, proliferation history separated candidate HSCs into units with distinct molecular and functional attributes. Our data thereby highlight that HSC proliferation following transplantation is fundamentally different not only from native hematopoiesis but also from other stress contexts, and demonstrate the power of divisional history as a functional criterion to resolve HSC heterogeneity
About 1000 genes are measured in GSE77477

Monday, May 15, 2017

Jackson lab genomics Day 1


install anaconda3 on ubuntu virtualbox

hqin@rainboxdash:~/anaconda3/bin/$ ./jupter notebook & 

samtools view example.bam | less

Linux exercises: up and down arrows, tab for file-names autocomplete

bam file

vcf file (variant call file?)

less test.vcf

mkdir tmpdir
cd tmpdir
cd ..

Essential probability and statistics for introduction to big data

  • summary stattistics vs empirical statistics
  • Common data transformation, Z-scores
  • Bayesian inference
  • Multiple hypothesis testing

Bayes's rule

Introductory Data Mining


 all biology are computational
 computational biology are parasite of biology

  research project approach using real datasets
  targeted students: professional and academic oriented?

data carpentry's R for genomcis

RStudio projects


Barke Southern Illinois Medical School, longest-living mouse

Brenton Gravely, RNA genomics

Dscam, over 100 exons, an extreme case of alternative splicing
Schmucker 2000 Cell. Mutually exclusive splicing.
Ig Repeats.  Dimerization are isoform specific.

Dscam variatiosn between species
Gravely 2005, Cell.

Competing RNA base-pairing is a common mechanism for mutually exclusive splicing in anthropods, Yang 2011 Nature Struc Mol Biol

single cell RNA sequencing
Drosophila S2 cells, each cell show the same splicing isoform.

Drop-seq of Drosophila and human cell to control the number of cells in each droplet.

Oxford nanopore sequencing

1500 RNA binding proteins in human genome

Van Nostrand Nature methods, 2016, eCLIP-seq reveqls RBP-specific binding profiles

Sunday, May 14, 2017

sample RCN

Emory , RCN-UBE The Case Study and PBL Network

Joey Shawn
Award Abstract #1410087 
 Digitization TCN: Collaborative Research: The Key to the Cabinets: Building and Sustaining a Research Database for a Global Biodiversity Hotspot

important concepts/skills in undergraduate QBIO education and training

Important concepts

  • Modeling approach:
    •  ODE
    •  PDE
    •  Discrete
    •  Analytic versus simulation
  • Bistability, bifurcation
  • Visualization of quantitative models

NSF RCN elements

ll RCN proposals (including RCN-UBE) must conform to the following 7 guidance items:
  1. Topic/focus of research coordination. For all tracks, research coordination network (RCN) proposals should identify a clear theme as the focus of its activities. RCN proposals should spell out the theoretical and/or methodological foundations of the network's proposed activities, and should specify what activities will be undertaken, what new groups of investigators will be brought together, what products will be generated by network activities, and how information about the network and opportunities to participate will be disseminated. The proposal should also outline the expected benefits of the network's activities in moving a field forward and the implications for the broader community of researchers, educators and engineers.
  2. Principal investigator (PI). Although research coordination networks are expected to involve investigators from multiple sites, a single organization must serve as the submitting organization for each proposal. Of the two types of collaborative proposal formats described in the Grant Proposal Guide, this solicitation allows only a single proposal submission with subawards administered by that lead organization. The PI is the designated contact person for the project and is expected to provide leadership in fully coordinating and integrating the activities of the network. Strong, central leadership and clear lines of responsibility are essential for successful networking.
  3. Steering committee. Members of the steering committee will be network participants that assume key roles in the leadership and/or management of the project. The steering committee should be representative of the communities of participants that will be brought together through the RCN. It must include all Co-PIs, if any are listed on the cover page of the proposal, and any other senior personnel, including any foreign collaborators involved as leaders or otherwise considered senior personnel. Therefore, the steering committee constitutes all the senior personnel for the RCN proposal. The name and home organization of each steering committee member should be listed in the project summary. As these individuals are all senior personnel, their Biographical Sketches and Current and Pending Support statements must be included in the appropriate sections of the proposal.
  4. Network participants. The size of a network is expected to vary depending on the theme and the needs of the proposed activity. The network may be regional, national, or international. It is expected that a proposed network will involve investigators at diverse organizations. The inclusion of new researchers, post-docs, graduate students, and undergraduates is encouraged. Specific efforts to increase participation of underrepresented groups (women, underrepresented minorities, and persons with disabilities) must be included. In the proposal, an initial network of likely participants should be identified. However, there should be clearly developed mechanisms to maintain openness, ensure access, and actively promote participation by interested parties outside of the initial participants in the proposed network.
  5. Coordination/management mechanism. The proposal should include a clearly defined management plan. The plan should include a description of the specific roles and responsibilities of the PI and the steering committee. Mechanisms for allocating funds, such as support for the work of a steering committee, should be clearly articulated. The plan should include provisions for flexibility to allow the structure of the participant group to change over time as membership and the network's foci evolve. Mechanisms for assessing progress and the effectiveness of the networking activities should be part of the management plan.
  6. Information and material sharing. The goals of this program are to promote effective communication and to enhance opportunities for collaboration. Proposers are expected to develop and present a clearly delineated understanding of individual member's rights to ideas, information, data and materials produced as a result of the award that is consistent with the goals of the program. Infrastructure plans to support the communication and collaboration should be described. When the proposed activity involves generation of community resources such as databases or unique materials, a plan for their timely release and the mechanism of sharing beyond the membership of the RCN must be described in the Data Management Plan, a required Supplementary Document. In addition, a plan for long-term maintenance of such resources must be described without assuming continued support from NSF.
  7. International participation. NSF encourages international collaboration, and we anticipate that many RCN projects will include participants, including steering committee members, from outside the US. International collaborations should clearly strengthen the proposed project activities. As NSF funding predominantly supports participation by US participants, network participants from institutions outside the US are encouraged to seek support from their respective funding organizations, notably participants from developed countries. NSF funds may not be used to support the expenses of the international scientists and students at their home organization. For RCN projects that involve international partners, NSF funds may be used for the following:
Travel expenses for US scientists and students participating in exchange visits integral to the RCN project
RCN-related expenses for international partners to participate in networking activities while in the US.
RCN-related expenses for US participants to conduct networking activities in the international partner's home laboratory.

Friday, May 12, 2017

*** R learning materials and computational biology

Online courses

Github applied computational genomics

R programing at Coursera

Data camp 
 introduction, intermediate, and advanced R

Statistics and R

Quantitative biology workshop

Introduction to Bioconductor: annotation and analysis of genomes and genomics assays . R book, primer to analysis of genomics data using R

Books and articles
An introduction to statistical learning with applications in R


student progress

KR: finished data camp R intro course. next week, intermediate level,

BD: datacamp R intro.
 Find RNAseq youtube tutorials and readings, write a brief comments on these tutorials.

Install VirtualBox, Ubuntu

Wednesday, May 10, 2017

App to estimate volume, conver units.

Many app exists for unit conversions

photo, video volume estimation

handwriting recognition apps

making student thinking and learning visible

Ken Shelton


Student Portfolios

Assessment for, as, of Learning
For learning (Formative)
Of Learning (summative)
As Learning (Reflective)

Digital Portfolio can let students

Monday, May 8, 2017

BD tasks

Learning R
  Datacamp, intro, intermediate, advanced

Find reading materials on RNAseq


  Coursera R training course
 Virtual Box, Ubuntun
 Learning Linux

20170508, BD borrowed Haddock and Dunn, practical computing for biologist.

Sunday, May 7, 2017

*** computational genomics course plan

data visualization book

Key topics
R/Rstudio Rmd

Regular expression online exercise

biological network

Potential student projects: 
time lapsed image analysis
rDNA reads in yeast genomes ~ lifespan
chemical compounds
network controllability
yeast RLS ~ genomics features
prediction of essential and non-essential genes.
ecology network analysis
aging data comparison
RNN reverse engineering of gene interactions

Jackson lab workshop
Data Carpentry, 

EdX genomics course

Friday, May 5, 2017

UTC grading scale for letter grade

UTC grading policy, grading scale
90-100 A
80-89 B
70-79 C
60-69 D
Below 60 F

Wednesday, May 3, 2017

student project, time lapsed image

MATLAB, code from image analysis

TODO: tracking objects around images

KR student project

=> Yeast RLS DR fitting

=> Raspberry Pi, 4273 Pi

IPTG ordering
IPTG ordering
 > 99% purity, dioxane-free
 High efficency on induction of protein expression and blue-white screening in E. coli
 Available at Fisher Scientific (Fisher Cat# 50-112-6936, 50-112-6935, 50-112-6934)
 Large quantity available (up to 100 KG)
 Hugh savings compared to other vendors (see price comparison below)

Price comparison (prices were obtained from vendors' websites)

Packing SizeUBPBioPromegaEMD MilliporeSigma
1 gN/A $52$62.5$63.2
5 gN/A $199$164$279
10 g$50.00N/A$226$474.50
25 g$100.00N/A $473N/A
100 g$300.00 N/A$1,153N/A

UBPBio   12635 E. Montview Blvd, STE 222, Aurora, CO 80045, USA   Email:    Web:  

Tuesday, May 2, 2017

rls 20160802.db missing entries

The rls 20160802.db contains many missing entries.  I export this db using sqlite3 into csv, and these missing entries are still there. So, the problem is not limited to csv, but is due to this release of rls.db.

UTC international undergraduate application

transcripts WES 



Monday, May 1, 2017

JAX computational genomics tools

On the academic side:

We will be using a number of genomic analysis software packages/tools. Please try to download and install the tools/programs listed below (IGV, R/RStudio and Python).  Ada Zhan (cc’ed here) can assist you with installation questions. We will also be able to provide support on the first day of the course. We will use a cloud computing environment (web-based) but you will get information on that platform just before the course.

If you do not have a laptop at your disposal please alert me ASAP so that we can prepare a machine for your use.

Please install the following:

Integrative Genomics Viewer: (IGV) (Broad Institute)

Please go to the Broad institute website here and download the IGV version for your Mac or PC.


R is a programming language that is especially powerful for data exploration, visualization, and statistical analysis. To interact with R, we use RStudio   To install on:


Mac OS X:


Python:  To set  up Python:


Mac OS X


  1. Download the installer that matches your operating system and save it in your home folder. Download the default Python 3 installer.
  2. Open a terminal window.
  3. Type
bash Anaconda-
  1. Press enter. You will follow the text-only prompts. When there is a colon at the bottom of the screen press the down arrow to move down through the text. Type yes and press enter to approve the license. Press enter to approve the default location for the files. Type yes and press enter to prepend Anaconda to your PATH (this makes the Anaconda distribution the default Python).

Desmos Calculator

math education