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Item	Cost	Number of experiments
Pack 4xPromethION Flow Cell	$4000	4-40
(multiplexing) cDNA-PCR Barcoding Kit V14	$750	144
(direct RNA) Direct RNA Sequencing Kit	$600	6
Induro® Reverse Transcriptase and 5x Induro® RT Reaction Buffer (NEB, M0681)	$200	20
RNAse inhibitor	$600	100
dNTP mix	$300	600
NEBNext® Quick Ligation Module	$400	20
[RNAClean XP RNA and cDNA Cleanup Reagent, 40 mL](http

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Droplet-based barcoding: 100 - 100k cells (10x genomics)

Item	Cost	Number of experiments
GEM-X Universal 3' Gene Expression v4, 16 samples	$24500	16
Chromium GEM-X Single Cell 3' Chip Kit v4, 4 chips	$1400	32
Dual Index Kit TT Set A, 96 rxn	$1100	96
500M 2x150 reads (25k/cell) on Aviti Medium Output	$1100	1
Total per xp (20k cells)	$2700	1
Cost per cell (25k reads)	$0.14

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|Item|Cost|Number of experiments|
|---------|--------|--------|
|[GEM-X Universal 3' Gene Expression v4, 16 samples](https://www.10xgenomics.com/store/experiment-builder?assay=ThreePrime&version=V40&step=form)|\$24500|16|
|[Chromium GEM-X Single Cell 3' Chip Kit v4, 4 chips](https://www.10xgenomics.com/store/experiment-builder?assay=ThreePrime&version=V40&step=form)|\$1400|32|
|[Dual Index Kit TT Set A, 96 rxn](https://www.10xgenomics.com/store/experiment-builder?assay=ThreePrime&version=V40&step=form)|\$1100|96|
|[500M 2x150 reads (25k/cell) on Aviti Medium Output](https://www.elementbiosciences.com/products/aviti/specs)|\$1100|1|
|**Total per xp (20k cells)**|\$2700|1|
|**Cost per cell (25k reads)**|\$0.14||

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Item	Cost	Number of experiments
Monarch® Total RNA Miniprep Kit	300	50
NEBNext® rRNA Depletion Kit	1170	24
NEBNext Ultra II Directional RNA Library Prep Kit Illumina	1100	24
NEBNext® Multiplex Oligos for Illumina®	120	24

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def deepSv_predict(reads, reference, representation, model):
  stats = generate_read_stats(reads) # we grab std and mean for insert and read size
  candidates = paired_read_analysis(reads, stats) # generate candidate variants
  
  calls = set()
  for (variant, supporting_reads) in candidates:
    proba = model.predict(representation.generate(variant, supporting_reads))
    if proba > 0.5:
      calls.add((variant, supporting_reads))
      

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def deepSv_predict(reads, reference, representation, model):
  stats = generate_read_stats(reads) # we grab std and mean for insert and read size
  candidates = paired_read_analysis(reads, stats) # generate candidate variants
  
  calls = set()
  for (variant, supporting_reads) in candidates:
    proba = model.predict(representation.generate(variant, supporting_reads))
    if proba > 0.5:
      calls.add((variant, supporting_reads))
      

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#include <iostream>
#include <sstream>
#include <string>
#include <vector>
#include <climits>

using namespace std;

int main() {

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use std::any::Any;
use std::collections::VecDeque;

enum Message {
    Command(String),
    Data(Box<Any>),
}

trait Actor {
    fn receive(&mut self, Message);

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#include “hal.h”
#include “ch.h”

int main(void) {
  halInit(); // initializes hardware abstraction layer
  chSysInit(); // initializes ChibiOS kernel
  // configures GPIOC pin 1 as output pushpull
  palSetPadMode(GPIOC, 1, PAL_MODE_OUTPUT_PUSHPULL);
  while(1) {
    palSetPad(GPIOC, 1); // sets GPIOC 1 high

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/* Simple PWM example */

/*
* PWMD3 is a HAL defined variable of type PWMDriver - it is associated with
* TIM3.
*/

#include "ch.h"
#include "hal.h"