Video Analysis of Invertebrates’ Movement

image

27 Jan, 2020 @ 9:30 am 31 Jan, 2020 @ 5:00 pm

image

Details

Start:
27 Jan, 2020 @ 9:30 am
End:
31 Jan, 2020 @ 5:00 pm
Cost:
€260 – €350

Modica

Modica, 97015 Italy + Google Map
More details soon…

Outline

In this course, you will learn how to analyze the movement and behavior of invertebrates from videos. The aim is to offer you a new analytical tool to study the causes and consequences of movement in small organisms, ranging from insects to zooplankton. The course will cover all the salient aspects of video analysis, starting with basic and advanced video filming techniques (data acquisition), experimental design, and analytical pipelines for testing movement and behavior under different environmental conditions, and at population and community level. Original data will be collected and analyzed, plus a published dataset (DOI:10.4228/ZALF.DK.92) will be explored during the course. 

After the course, the attendee will be able to acquire and manipulate video files under a solid, automatic analytical workflow, to modify and create custom ImageJ macros, to run two different auto-tracking routines and to explore and analyse movement data (Type II trajectories).  

Basic experience with R is required to attend the course.

Themes:

  • filming techniques and video setup
  • experimental design
  • automatic trajectory extraction with R and ImageJ
  • data cleaning and visualization (gganimate)
  • statistical methods of movement analysis

Calendar:

Day 1:

Tracking particles in video data:

  • Observing behavior in small organisms
  • Filming techniques
  • The camera (shutter, diaphragm, ISO speed)
  • Introduction to R, ImageJ & ffmpeg
  • Introduction to video file formats and databases
  • Movement and morphology descriptors
  • Capturing moving particles in manual tracking (TrackMate)

Day 2:

Editing video files:

  • ImageJ basics
  • Particle analysis
  • Binary video operations
  • Batch analysis
  • Macro scripting
  • Video enhancement, size compression

Day 3:

Introduction to BEMOVI (R package):

  • Video files batch conversion
  • Folder structure and data input/output
  • The BEMOVI pipeline
  • Error filtering – trajectory clean-up

Day 4:

Introduction to TrackDem (R package) :

  • Particles simulations
  • The TrackDem pipeline
  • Error filtering with neural networks
  • From arrays to dataframes

Day 5:

From code to interpretation:

  • Statistics for movement data
  • Movement parameters (speed, turning angles…)
  • HMM models (state-space models)
  • Fit a distribution

Data visualization:

  • Static vs. dynamic graphics
  • Basic and advanced trajectories visualization
  • Base R and ggplot
  • Graph animation (gganimate)
  • GIF output for data presentation

daphnia under UV stress

Trajectories of Daphnia magna under visible light (left) and UVA radiation (right). Colangeli et al. 2016.

daphnia UV test

Relevant literature:

Pennekamp et al. 2015. “BEMOVI, software for extracting behavior and morphology from videos, illustrated with analyses of microbes.” Ecology and Evolution

Colangeli et al. 2018. “Negative phototactic response to UVR in three cosmopolitan rotifers: a video analysis approach.” Hydrobiologia


Instructor:

Colangeli Pierluigi, Ph.D. 

Hyblea Training, Italy

Topics: plankton ecology, movement and behavior analysis, biodiversity


EARLY-BIRDS DEADLINE: 15 OCT 2019
REGISTRATION DEADLINE: 15 DEC 2019

Cancellation policy:

Up to 30 days before the beginning of the course: complete ticket price refund (minus billing operations, depending on the payment method).

Less than 30 days before the beginning of the course: no refund available.

HT is not refunding travel and accomodation costs. 

ImageJ for Biologists

imagej course

02 Dec, 2019 @ 9:30 am 04 Dec, 2019 @ 5:00 pm

imagej course

Details

Start:
02 Dec, 2019 @ 9:30 am
End:
04 Dec, 2019 @ 5:00 pm
Cost:
€170

Modica

Modica, 97015 Italy + Google Map
More details soon…

Outline

Counting and measuring particles like cells and individuals from a population is a time-consuming process which is a necessary step in several research fields. Automatization can increase accuracy and reduce the time spent on such a process. The open-source software ImageJ is a powerful tool for image, images stacks, time-lapse and video analysis. 

This course will serve as an introduction to image analysis workflows, spanning from basic particle counting to custom macro design. After the course, the student will be able to integrate image analysis in his experimental routine.

Themes:

  • Image analysis basics
  • Particle analysis
  • Data import in R
  • Data visualization with ggplot
  • Random forest classification

Calendar:

Day 1:

Intro to ImageJ:

  • Image acquisition tips and tricks
  • Import images in ImageJ
  • Introduction to the available commands
  • Particle analysis
  • Morphological descriptors

Day 2:

ImageJ customization:

  • Macro language and design
  • Available plugins
  • Intro to video analysis
  • Intro to particle tracking

Day 3:

From ImageJ to R:

Notes:

A notebook is required. The installation of ImageJ and R is suggested. 

Basic particle analysis routine in Maple plant stem cross-section.

imagej tutorial

Tracking embolism events in poplar leaf veins using the open-source optical technique.

leaf cavitation exposed


Hyblea Training logoInstructors:

Pierluigi Colangeli, Ph.D.

Silvia Lechthaler, Ph.D.

Co-founders of Hyblea Training

Topics: image analysis, particle analysis, automatization


REGISTRATION DEADLINE: 1 NOV 2019

Scientific Writing

18 Nov, 2019 @ 9:30 am 20 Nov, 2019 @ 6:00 pm

Details

Start:
18 Nov, 2019 @ 9:30 am
End:
20 Nov, 2019 @ 6:00 pm
Cost:
€200 – €250

Palazzo Failla Hotel

via Blandini 5
Modica, 97015 Italy
+ Google Map

Outline

Writing is a fundamental component of scientific research. Communicating science effectively in the written format is a substantial skill that researchers should master. Scientific communication has basic but essential rules which are key to successful publishing. In this course, you will learn how to communicate your scientific results in a written format and enhance your chances of publication. The course will cover all the necessary steps that precede the publication of a scientific paper:

  • IMRaD (structure of an article)
  • solving common errors in scientific writing
  • authorship
  • review process (how to cope with cover letters and revisions)
  • journal selection strategies

Ulrike OberteggerInstructor
Obertegger Ulrike, Ph.D.

Edmund Mach Foundation, Italy

Topics: time-series analysis, plankton ecology, movement and behavior analysis, limnology, biodiversity


REGISTRATION DEADLINE: 11 OCT 2019
EARLY-BIRDS DEADLINE: 10 AUG 2019

Tickets

The numbers below include tickets for this event already in your cart. Clicking “Get Tickets” will allow you to edit any existing attendee information as well as change ticket quantities.
Ticket
Course only
250,00
Early birds
Course only
200,00

Data Exploration and Visualization in R with tidyverse & ggplot2

04 Nov, 2019 @ 9:30 am 08 Nov, 2019 @ 5:00 pm

Details

Start:
04 Nov, 2019 @ 9:30 am
End:
08 Nov, 2019 @ 5:00 pm
Cost:
€260

Modica

Modica, 97015 Italy + Google Map
More details soon…

Outline

The upsurge of data sets complexity in evolutionary biology requires researchers the ability to organize, explore and visualize information in an efficient and reproducible way. Data exploration and visualization is a fundamental component of data science and scientific research. Extracting knowledge and insights from structured and unstructured biological data is a key skill and requires not only a good understanding of the scientific methods, but also the ability to process and visualize data in a reproducible way.

In this course, you will learn how to explore, transform, analyze and visualize different data sets to gain knowledge and to communicate these insights to a scientific and/or general audience. The course will cover all the necessary steps that are needed for data exploration and visualization in R:

  • Introduction to R, RStudio and Version Control
  • Working with different data types
  • Exploration and transformation of data using “tidyverse”
  • Fitting simple models to analyse data
  • Data visualization using “ggplot2”

Targeted Audience

This workshop is aimed at researchers and technical workers in biology and related sub-fields, although the content of the course is relevant to other similar data practitioners. In general, no programming experience is needed. The course teaches all relevant steps to load, transform, explore, visualize and analyze the data using R and RStudio.

Calendar:

Day 1: Introduction to R, RStudio and Version Control

Day 2: Data transformation using “tidyverse”

Day 3: Data visualization using “tidyverse” and “ggplot2”

Day 4: Data models using “tidyverse”

Day 5: Exploration and visualization of own (or provided) data


img-CedricInstructor:

Cédric Scherer, Ph.D. @ BioMove

IZW Berlin, Germany

Topics: Individual based modelling, movement ecology, programming


NOTES ON ACCOMMODATION

Please note that the city of Modica has a rather complex topography, therefore we suggest you to book your stay in a structure located in the old town, preferably close to the venue of the course.

EARLY-BIRDS DEADLINE: 15 SEPT 2019
REGISTRATION DEADLINE: 01 OCT 2019

Tickets

The numbers below include tickets for this event already in your cart. Clicking “Get Tickets” will allow you to edit any existing attendee information as well as change ticket quantities.
Ticket
Course only
250,00
Early birds
Course only
200,00
Cancellation policy:

Up to 30 days before the beginning of the course: complete ticket price refund (minus billing operations, depending on the payment method).

Less than 30 days before the beginning of the course: no refund available.

HT is not refunding travel and accomodation costs. 

Bioinformatics for Next-Generation Sequencing

NGS

26 Aug, 2019 @ 9:30 am 30 Aug, 2019 @ 6:00 pm

NGS

Details

Start:
26 Aug, 2019 @ 9:30 am
End:
30 Aug, 2019 @ 6:00 pm
Cost:
€310

San Nicolò ed Erasmo

Corso Regina Margherita
Modica, 97015 Italy
+ Google Map

Outline

Next-Generation Sequencing (NGS) has become an essential tool in genetic and genomic analysis. It is increasingly important for experimental scientists to gain the bioinformatics skills required to assess and analyze the large volumes of sequencing data produced by next-generation sequencers.

Advantages and disadvantages of current sequencing technologies and their implications on data analysis will be discovered.

This course will provide an introduction to the technology, analysis workflows, tools and resources for Next Generation Sequencing data analysis. The content will provide insights into how biological knowledge can be derived from genomics experiments and explain different approaches to analyzing such data.

Themes:

  • NGS ABC’s
  • R-python workflow
  • data visualization
  • from code to interpretation

Calendar:

Day 1:

Linux for Bioinformatics:

  • Introduction to the command line and important commands
  • Combining commands by piping and redirection
  • Introduction to R and Python
  • Introduction to bioinformatics file formats and databases
  • Usage of important bioinformatics toolkits

Day 2:

Introduction to NGS data analysis:

  • Introduction to sequencing technologies
  • Raw sequence files (FASTQ format)
  • Preprocessing of raw reads: quality control – adapter clipping – quality trimming
  • Introduction to read mapping
  • Read mapping (Bowtie – BWA – STAR)

Day 3:

Introduction to NGS data analysis :

  • Mapping output (SAM/BAM format)
  • Usage of NGS toolkit (samtools – BEDtools – Picard tools)
  • Mapping Statistics
  • Visualization of mapped read

Day 4:

Variant Calling – RNASeq Data Analysis:

  • DNA variant calling methods and tools
  • Variant Call File Format
  • Filtering and annotation of genetic variants
  • Split-read mapping
  • Tuxedo suite
  • Statistics behind DESeq2 – EdgeR
  • Quantify exons/genes/transcripts
  • Predict differential splicing
  • Differential gene expression using DESeq2 / EdgeR
  • Differential isoform expression using cuffdiff
  • Create graphics using R

Day 5:

ChIP-Seq – Metagenomics:

  • ChIP-Seq:
    • Experimental design
    • Mapping software
    • Peak calling
    • Analysis of enriched area
    • Viewing ChIP-Seq data in genome browsers
  • Metagenomics:
    • Overview of existing methods for metagenomics data analysis
    • Run QC: run assessment
    • Read QC: metrics for read quality evaluation
    • Preprocessing of raw data
    • Mapping reads to a reference database
    • Examination of the community composition (taxonomic and functional)
    • Evaluation of the community saturation and diversity
DOWNLOAD PROGRAM

 

Notes:

Preferred Operative System: Linux – Ubuntu. Windows users might consider a dual-boot installation or using a virtual machine. The installation of Anaconda and R is also suggested.

bioinformatics-ngs

 

Relevant literature:

Giurato et al. 2013. “iMir: an integrated pipeline for high-throughput analysis of small non-coding RNA data obtained by smallRNA-Seq.” BMC bioinformatics

Giurato et al. (in press). “Quantitative mapping of RNA-mediated nuclear Estrogen Receptor β interactome in human breast cancer cells.” Scientific Data (ISSN:2052-4463)


Instructor:

Giorgio Giurato, Ph.D.

University of Salerno, Italy – Senior Bioinformatician

Genomix4life – Co-founder

Topics: bioinformatics, molecular medicine, NGS, RNA


This event is supported by the Municipality of Modica

EARLY BIRDS DEADLINE: EXPIRED

EXTENDED DEADLINE: 1 AUG 2019

Tickets

The numbers below include tickets for this event already in your cart. Clicking “Get Tickets” will allow you to edit any existing attendee information as well as change ticket quantities.
Ticket
Course only
250,00
Early birds
Course only
200,00

Video Analysis of Invertebrates’ Movement

video analysis

24 Jun, 2019 @ 9:30 am 28 Jun, 2019 @ 6:00 pm

video analysis

Details

Start:
24 Jun, 2019 @ 9:30 am
End:
28 Jun, 2019 @ 6:00 pm
Cost:
€310 – €350

Palazzo Failla Hotel

via Blandini 5
Modica, 97015 Italy
+ Google Map

Outline

In this course, you will learn how to analyze the movement and behavior of invertebrates from videos. The aim is to offer you a new analytical tool to study the causes and consequences of movement in small organisms, ranging from insect to zooplankton. The course will cover all the salient aspects of video analysis, starting with basic and advanced video filming techniques (data acquisition), experimental design, and analytical pipelines for testing movement and behavior under different environmental conditions, and at population and community level. Original data will be collected and analyzed, plus a published dataset (DOI:10.4228/ZALF.DK.92) will be explored during the course. 

Themes:

  • filming techniques and video setup
  • experimental design
  • trajectory extraction with R and ImageJ (BEMOVI package)
  • data cleaning and visualization (gganimate)
  • statistical methods of movement analysis

Calendar:

Day 1:

Movement ecology and behavior:

  • Observing behavior in small organisms
  • Filming techniques
  • Introduction to R, ImageJ & ffmpeg
  • Introduction to video file formats and databases
  • Movement and morphology descriptors

Day 2:

Data acquisition:

  • Experimental design
  • Filming techniques (bright light, dark field…)
  • Find best signal-to-noise ratio
  • Trigger dramatic responses

Day 3:

Introduction to BEMOVI :

  • Video files batch conversion
  • Contrast, exposure, gamma, saturation
  • Folder structure and data input/output
  • The BEMOVI pipeline
  • Error filtering – trajectory clean-up

Day 4:

Data visualization:

  • Static vs. dynamic graphics
  • Basic and advanced trajectories visualization
  • Base R and ggplot
  • Graph animation (gganimate)
  • GIF output for data presentation

Day 5:

From code to interpretation:

  • Statistics for movement data
  • Movement parameters (speed, turning angles…)
  • HMM models (state-space models)
  • Fit a distribution

daphnia under UV stress

Trajectories of Daphnia magna under visible light (left) and UVA radiation (right). Colangeli et al. 2016.

daphnia UV test

Relevant literature:

Pennekamp et al. 2015. “BEMOVI, software for extracting behavior and morphology from videos, illustrated with analyses of microbes.” Ecology and Evolution

Colangeli et al. 2018. “Negative phototactic response to UVR in three cosmopolitan rotifers: a video analysis approach.” Hydrobiologia


Instructor:

Colangeli Pierluigi, Ph.D. @ BioMove

Hyblea Training, Italy

Topics: plankton ecology, movement and behavior analysis, biodiversity


REGISTRATION DEADLINE: 10 JUNE

Tickets

The numbers below include tickets for this event already in your cart. Clicking “Get Tickets” will allow you to edit any existing attendee information as well as change ticket quantities.
Ticket
Course only
250,00
Early birds
Course only
200,00

gganimate tutorial

This code shows how to animate rotifers trajectories extracted with BEMOVI via gganimate

Exported from Notepad++
# Install needed packages install.packages(c("gganimate","glue","rlang","stringi","gifski","viridis")) # Load gganimate and viridis, ggplot2 will be loaded automatically library(gganimate) library(ggplot2) library(viridis) # Import the Brachionus calyciflorus dataset BRA = read.delim("C:/.../BRA.txt") # Build the ggplot base graph anim_1 = ggplot(BRA, aes(X, Y)) # Facet grid to show sexes, colorize according to direction, add styling anim_2 = anim_1 + geom_point(aes(size=abs_angle, colour=factor(round(abs_angle)))) + scale_size(range = c(2, 5)) + facet_grid(.~SEX) + scale_color_viridis(discrete=TRUE) + theme( plot.title = element_text(color="white",hjust=0, vjust=1, size=rel(2)), plot.background = element_rect(fill="gray20"), panel.background = element_rect(fill="gray20"), panel.border = element_rect(fill=NA,color="gray50", size=0.5, linetype="solid"), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.ticks = element_blank(), axis.title=element_blank(), axis.text=element_blank(), strip.text.x = element_text( size = 12, face = "bold"), legend.position = "none") # Animate the frames of the graph anim_3=anim_2 + transition_time(frame) + labs(title = "Frame: {frame_time}") + shadow_wake(wake_length = 0.125, alpha = 0.4) # Lastly, get the animation (takes a while...) animate(anim_3, fps = 20, width = 800, height = 420)